diff --git a/crates/miroir-core/src/scatter.rs b/crates/miroir-core/src/scatter.rs
index 2e76bb8..74f4967 100644
--- a/crates/miroir-core/src/scatter.rs
+++ b/crates/miroir-core/src/scatter.rs
@@ -1,7 +1,7 @@
 //! Scatter orchestration: fan-out logic and covering set builder.
 
 use crate::config::UnavailableShardPolicy;
-use crate::merger::ShardHitPage;
+use crate::merger::{merge, MergeInput, MergedSearchResult, ShardHitPage};
 use crate::router::{covering_set, query_group};
 use crate::topology::{NodeId, Topology};
 use crate::Result;
@@ -292,6 +292,55 @@ pub async fn execute_scatter<C: NodeClient>(
     })
 }
 
+/// Execute a full scatter-gather search: fan out to nodes, then RRF-merge results.
+///
+/// This is the primary entry point for the read path. It combines
+/// `execute_scatter` (fan-out) with `merge` (RRF result merging)
+/// into a single operation.
+///
+/// # Arguments
+/// * `plan` - Scatter plan from `plan_search_scatter`
+/// * `client` - HTTP client for communicating with nodes
+/// * `req` - Search request to execute
+/// * `topology` - Current topology (for resolving node addresses)
+/// * `policy` - Policy for handling unavailable shards
+///
+/// # Returns
+/// A `MergedSearchResult` with globally ranked hits using RRF.
+pub async fn scatter_gather_search<C: NodeClient>(
+    plan: ScatterPlan,
+    client: &C,
+    req: SearchRequest,
+    topology: &Topology,
+    policy: UnavailableShardPolicy,
+) -> Result<MergedSearchResult> {
+    let scatter_result = execute_scatter(plan, client, req.clone(), topology, policy).await?;
+
+    // Mark failed shards as degraded in the shard pages
+    let mut shard_pages = scatter_result.shard_pages;
+    if scatter_result.partial {
+        // Add failed shard markers so the merger sets the degraded flag
+        for shard_id in scatter_result.failed_shards.keys() {
+            shard_pages.push(ShardHitPage {
+                body: serde_json::json!({
+                    "success": false,
+                    "message": format!("shard {} unavailable", shard_id),
+                }),
+            });
+        }
+    }
+
+    let merge_input = MergeInput {
+        shard_hits: shard_pages,
+        offset: req.offset,
+        limit: req.limit,
+        client_requested_score: req.ranking_score,
+        facets: req.facets.clone(),
+    };
+
+    merge(merge_input)
+}
+
 /// Stubs for testing (no actual network calls).
 
 /// Mock `NodeClient` for testing.
@@ -546,4 +595,98 @@ mod tests {
         let net_err = NodeError::NetworkError("connection refused".to_string());
         assert!(matches!(net_err, NodeError::NetworkError(_)));
     }
+
+    #[tokio::test]
+    async fn test_scatter_gather_search_rrf_merge() {
+        let topo = make_test_topology();
+        let plan = plan_search_scatter(&topo, 0, 2, 64);
+
+        let mut client = MockNodeClient::default();
+        // Each node returns different hits
+        client.responses.insert(
+            NodeId::new("node-0".to_string()),
+            serde_json::json!({
+                "hits": [
+                    {"id": "doc-a", "title": "Doc A", "_rankingScore": 0.9},
+                    {"id": "doc-b", "title": "Doc B", "_rankingScore": 0.7},
+                ],
+                "estimatedTotalHits": 2,
+                "processingTimeMs": 5,
+            }),
+        );
+        client.responses.insert(
+            NodeId::new("node-1".to_string()),
+            serde_json::json!({
+                "hits": [{"id": "doc-c", "title": "Doc C", "_rankingScore": 0.8}],
+                "estimatedTotalHits": 1,
+                "processingTimeMs": 3,
+            }),
+        );
+        client.responses.insert(
+            NodeId::new("node-2".to_string()),
+            serde_json::json!({
+                "hits": [{"id": "doc-d", "title": "Doc D", "_rankingScore": 0.6}],
+                "estimatedTotalHits": 1,
+                "processingTimeMs": 4,
+            }),
+        );
+
+        let req = SearchRequest {
+            index_uid: "test".to_string(),
+            query: Some("test".to_string()),
+            offset: 0,
+            limit: 10,
+            filter: None,
+            facets: None,
+            ranking_score: false,
+            body: serde_json::json!({}),
+        };
+
+        let result = scatter_gather_search(plan, &client, req, &topo, UnavailableShardPolicy::Partial)
+            .await
+            .unwrap();
+
+        assert!(!result.degraded);
+        assert_eq!(result.hits.len(), 4); // 4 unique docs across all nodes
+        assert!(result.estimated_total_hits > 0);
+    }
+
+    #[tokio::test]
+    async fn test_scatter_gather_search_degraded() {
+        let topo = make_test_topology();
+        let plan = plan_search_scatter(&topo, 0, 2, 64);
+
+        let mut client = MockNodeClient::default();
+        client.responses.insert(
+            NodeId::new("node-0".to_string()),
+            serde_json::json!({
+                "hits": [{"id": "doc-a", "title": "Doc A"}],
+                "estimatedTotalHits": 1,
+                "processingTimeMs": 5,
+            }),
+        );
+        // node-1 and node-2 get default empty responses, but node-0 returns data
+        // Make node-2 fail
+        client.errors.insert(
+            NodeId::new("node-2".to_string()),
+            NodeError::Timeout,
+        );
+
+        let req = SearchRequest {
+            index_uid: "test".to_string(),
+            query: Some("test".to_string()),
+            offset: 0,
+            limit: 10,
+            filter: None,
+            facets: None,
+            ranking_score: false,
+            body: serde_json::json!({}),
+        };
+
+        let result = scatter_gather_search(plan, &client, req, &topo, UnavailableShardPolicy::Partial)
+            .await
+            .unwrap();
+
+        assert!(result.degraded);
+    }
 }
diff --git a/tests/benches/score-comparability/results/comparison-report-rrf.json b/tests/benches/score-comparability/results/comparison-report-rrf.json
new file mode 100644
index 0000000..c177572
--- /dev/null
+++ b/tests/benches/score-comparability/results/comparison-report-rrf.json
@@ -0,0 +1,11359 @@
+{
+  "total_queries": 806,
+  "avg_tau": 0.12414848507902454,
+  "min_tau": -0.16842105263157894,
+  "max_tau": 0.696969696969697,
+  "ci_95": [
+    0.11391439943732333,
+    0.13438257072072574
+  ],
+  "stddev": 0.14823832411660173,
+  "stderr": 0.005221472266174086,
+  "below_095_count": 806,
+  "below_090_count": 806,
+  "below_080_count": 806,
+  "pass_criteria": false,
+  "type_stats": {
+    "filtered": {
+      "count": 164,
+      "avg_tau": 0.07269360173577004,
+      "min_tau": -0.15789473684210525,
+      "max_tau": 0.5164835164835165,
+      "ci_95": [
+        0.055939525127271,
+        0.08944767834426907
+      ],
+      "stddev": 0.10946778980376212
+    },
+    "multi_term": {
+      "count": 193,
+      "avg_tau": 0.10357677895558083,
+      "min_tau": -0.16842105263157894,
+      "max_tau": 0.696969696969697,
+      "ci_95": [
+        0.08260216079165339,
+        0.12455139711950827
+      ],
+      "stddev": 0.14866770818492786
+    },
+    "single_term": {
+      "count": 216,
+      "avg_tau": 0.13040026741614738,
+      "min_tau": -0.0718954248366013,
+      "max_tau": 0.4857142857142857,
+      "ci_95": [
+        0.11218062053324977,
+        0.14861991429904498
+      ],
+      "stddev": 0.1366188923167016
+    },
+    "common_term": {
+      "count": 121,
+      "avg_tau": 0.12219692477349259,
+      "min_tau": -0.014705882352941176,
+      "max_tau": 0.45454545454545453,
+      "ci_95": [
+        0.08956200909575131,
+        0.15483184045123388
+      ],
+      "stddev": 0.1831551390077317
+    },
+    "rare_term": {
+      "count": 112,
+      "avg_tau": 0.22499396688499962,
+      "min_tau": 0.08823529411764706,
+      "max_tau": 0.4857142857142857,
+      "ci_95": [
+        0.2014722193151053,
+        0.24851571445489393
+      ],
+      "stddev": 0.12700549892158816
+    }
+  },
+  "per_query": [
+    {
+      "query_id": "q-filter-01674",
+      "query_type": "filtered",
+      "tau": -0.0196078431372549,
+      "details": {
+        "concordant": 75,
+        "discordant": 78,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "model"
+    },
+    {
+      "query_id": "q-multi-00389",
+      "query_type": "multi_term",
+      "tau": 0.34065934065934067,
+      "details": {
+        "concordant": 61,
+        "discordant": 30,
+        "total_pairs": 91,
+        "common_docs": 14,
+        "only_in_r1": 86,
+        "only_in_r2": 86
+      },
+      "query": "framework treatment laboratory symptom"
+    },
+    {
+      "query_id": "q-filter-01819",
+      "query_type": "filtered",
+      "tau": -0.04411764705882353,
+      "details": {
+        "concordant": 65,
+        "discordant": 71,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "platform"
+    },
+    {
+      "query_id": "q-single-01693",
+      "query_type": "single_term",
+      "tau": -0.007246376811594203,
+      "details": {
+        "concordant": 137,
+        "discordant": 139,
+        "total_pairs": 276,
+        "common_docs": 24,
+        "only_in_r1": 76,
+        "only_in_r2": 76
+      },
+      "query": "symptom"
+    },
+    {
+      "query_id": "q-common-00937",
+      "query_type": "common_term",
+      "tau": 0.45454545454545453,
+      "details": {
+        "concordant": 48,
+        "discordant": 18,
+        "total_pairs": 66,
+        "common_docs": 12,
+        "only_in_r1": 88,
+        "only_in_r2": 88
+      },
+      "query": "server"
+    },
+    {
+      "query_id": "q-filter-00381",
+      "query_type": "filtered",
+      "tau": 0.11764705882352941,
+      "details": {
+        "concordant": 76,
+        "discordant": 60,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "strategy"
+    },
+    {
+      "query_id": "q-rare-01062",
+      "query_type": "rare_term",
+      "tau": 0.08823529411764706,
+      "details": {
+        "concordant": 74,
+        "discordant": 62,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "model"
+    },
+    {
+      "query_id": "q-filter-01443",
+      "query_type": "filtered",
+      "tau": -0.021052631578947368,
+      "details": {
+        "concordant": 93,
+        "discordant": 97,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "strategy"
+    },
+    {
+      "query_id": "q-single-00324",
+      "query_type": "single_term",
+      "tau": -0.003952569169960474,
+      "details": {
+        "concordant": 126,
+        "discordant": 127,
+        "total_pairs": 253,
+        "common_docs": 23,
+        "only_in_r1": 77,
+        "only_in_r2": 77
+      },
+      "query": "trading"
+    },
+    {
+      "query_id": "q-filter-01342",
+      "query_type": "filtered",
+      "tau": 0.09523809523809523,
+      "details": {
+        "concordant": 115,
+        "discordant": 95,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "method"
+    },
+    {
+      "query_id": "q-multi-02150",
+      "query_type": "multi_term",
+      "tau": 0.12380952380952381,
+      "details": {
+        "concordant": 59,
+        "discordant": 46,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "database solution stock"
+    },
+    {
+      "query_id": "q-filter-00674",
+      "query_type": "filtered",
+      "tau": 0.06315789473684211,
+      "details": {
+        "concordant": 101,
+        "discordant": 89,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "symptom"
+    },
+    {
+      "query_id": "q-single-01724",
+      "query_type": "single_term",
+      "tau": 0.2,
+      "details": {
+        "concordant": 63,
+        "discordant": 42,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "framework"
+    },
+    {
+      "query_id": "q-single-02111",
+      "query_type": "single_term",
+      "tau": -0.003952569169960474,
+      "details": {
+        "concordant": 126,
+        "discordant": 127,
+        "total_pairs": 253,
+        "common_docs": 23,
+        "only_in_r1": 77,
+        "only_in_r2": 77
+      },
+      "query": "trading"
+    },
+    {
+      "query_id": "q-common-00352",
+      "query_type": "common_term",
+      "tau": 0.0989010989010989,
+      "details": {
+        "concordant": 50,
+        "discordant": 41,
+        "total_pairs": 91,
+        "common_docs": 14,
+        "only_in_r1": 86,
+        "only_in_r2": 86
+      },
+      "query": "database"
+    },
+    {
+      "query_id": "q-multi-00915",
+      "query_type": "multi_term",
+      "tau": 0.42424242424242425,
+      "details": {
+        "concordant": 47,
+        "discordant": 19,
+        "total_pairs": 66,
+        "common_docs": 12,
+        "only_in_r1": 88,
+        "only_in_r2": 88
+      },
+      "query": "stock operations customer algorithm"
+    },
+    {
+      "query_id": "q-multi-01427",
+      "query_type": "multi_term",
+      "tau": 0.04411764705882353,
+      "details": {
+        "concordant": 71,
+        "discordant": 65,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "customer growth data management"
+    },
+    {
+      "query_id": "q-rare-00300",
+      "query_type": "rare_term",
+      "tau": 0.4857142857142857,
+      "details": {
+        "concordant": 78,
+        "discordant": 27,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "platform"
+    },
+    {
+      "query_id": "q-single-01885",
+      "query_type": "single_term",
+      "tau": -0.007246376811594203,
+      "details": {
+        "concordant": 137,
+        "discordant": 139,
+        "total_pairs": 276,
+        "common_docs": 24,
+        "only_in_r1": 76,
+        "only_in_r2": 76
+      },
+      "query": "symptom"
+    },
+    {
+      "query_id": "q-filter-00753",
+      "query_type": "filtered",
+      "tau": -0.05714285714285714,
+      "details": {
+        "concordant": 99,
+        "discordant": 111,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "diagnosis"
+    },
+    {
+      "query_id": "q-multi-02281",
+      "query_type": "multi_term",
+      "tau": 0.3088235294117647,
+      "details": {
+        "concordant": 89,
+        "discordant": 47,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "design platform"
+    },
+    {
+      "query_id": "q-rare-01297",
+      "query_type": "rare_term",
+      "tau": 0.08823529411764706,
+      "details": {
+        "concordant": 74,
+        "discordant": 62,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "model"
+    },
+    {
+      "query_id": "q-single-01092",
+      "query_type": "single_term",
+      "tau": 0.12418300653594772,
+      "details": {
+        "concordant": 86,
+        "discordant": 67,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "medicine"
+    },
+    {
+      "query_id": "q-filter-00652",
+      "query_type": "filtered",
+      "tau": 0.005847953216374269,
+      "details": {
+        "concordant": 86,
+        "discordant": 85,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "treatment"
+    },
+    {
+      "query_id": "q-single-02379",
+      "query_type": "single_term",
+      "tau": 0.07368421052631578,
+      "details": {
+        "concordant": 102,
+        "discordant": 88,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "approach"
+    },
+    {
+      "query_id": "q-filter-00330",
+      "query_type": "filtered",
+      "tau": 0.10526315789473684,
+      "details": {
+        "concordant": 105,
+        "discordant": 85,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "code"
+    },
+    {
+      "query_id": "q-multi-00455",
+      "query_type": "multi_term",
+      "tau": -0.02857142857142857,
+      "details": {
+        "concordant": 102,
+        "discordant": 108,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "diagnosis solution discovery medicine"
+    },
+    {
+      "query_id": "q-filter-00869",
+      "query_type": "filtered",
+      "tau": 0.17647058823529413,
+      "details": {
+        "concordant": 90,
+        "discordant": 63,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "symptom"
+    },
+    {
+      "query_id": "q-filter-01661",
+      "query_type": "filtered",
+      "tau": 0.1895424836601307,
+      "details": {
+        "concordant": 91,
+        "discordant": 62,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "hypothesis"
+    },
+    {
+      "query_id": "q-single-02034",
+      "query_type": "single_term",
+      "tau": 0.08823529411764706,
+      "details": {
+        "concordant": 74,
+        "discordant": 62,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "data"
+    },
+    {
+      "query_id": "q-multi-00618",
+      "query_type": "multi_term",
+      "tau": -0.014705882352941176,
+      "details": {
+        "concordant": 67,
+        "discordant": 69,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "implementation approach"
+    },
+    {
+      "query_id": "q-single-00678",
+      "query_type": "single_term",
+      "tau": 0.058823529411764705,
+      "details": {
+        "concordant": 81,
+        "discordant": 72,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "product"
+    },
+    {
+      "query_id": "q-filter-00955",
+      "query_type": "filtered",
+      "tau": 0.14285714285714285,
+      "details": {
+        "concordant": 120,
+        "discordant": 90,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "cloud"
+    },
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+      "query_id": "q-single-01145",
+      "query_type": "single_term",
+      "tau": 0.07368421052631578,
+      "details": {
+        "concordant": 102,
+        "discordant": 88,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "approach"
+    },
+    {
+      "query_id": "q-filter-00672",
+      "query_type": "filtered",
+      "tau": 0.18095238095238095,
+      "details": {
+        "concordant": 62,
+        "discordant": 43,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "implementation"
+    },
+    {
+      "query_id": "q-multi-02212",
+      "query_type": "multi_term",
+      "tau": 0.1,
+      "details": {
+        "concordant": 66,
+        "discordant": 54,
+        "total_pairs": 120,
+        "common_docs": 16,
+        "only_in_r1": 84,
+        "only_in_r2": 84
+      },
+      "query": "growth symptom strategy"
+    },
+    {
+      "query_id": "q-single-00875",
+      "query_type": "single_term",
+      "tau": 0.02857142857142857,
+      "details": {
+        "concordant": 108,
+        "discordant": 102,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "network"
+    },
+    {
+      "query_id": "q-single-02015",
+      "query_type": "single_term",
+      "tau": -0.0718954248366013,
+      "details": {
+        "concordant": 71,
+        "discordant": 82,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "theory"
+    },
+    {
+      "query_id": "q-single-01435",
+      "query_type": "single_term",
+      "tau": 0.08333333333333333,
+      "details": {
+        "concordant": 65,
+        "discordant": 55,
+        "total_pairs": 120,
+        "common_docs": 16,
+        "only_in_r1": 84,
+        "only_in_r2": 84
+      },
+      "query": "dividend"
+    },
+    {
+      "query_id": "q-single-00345",
+      "query_type": "single_term",
+      "tau": 0.08823529411764706,
+      "details": {
+        "concordant": 74,
+        "discordant": 62,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "wellness"
+    },
+    {
+      "query_id": "q-common-01176",
+      "query_type": "common_term",
+      "tau": 0.45454545454545453,
+      "details": {
+        "concordant": 48,
+        "discordant": 18,
+        "total_pairs": 66,
+        "common_docs": 12,
+        "only_in_r1": 88,
+        "only_in_r2": 88
+      },
+      "query": "server"
+    },
+    {
+      "query_id": "q-multi-00610",
+      "query_type": "multi_term",
+      "tau": 0.029239766081871343,
+      "details": {
+        "concordant": 88,
+        "discordant": 83,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "discovery wellness"
+    },
+    {
+      "query_id": "q-filter-00842",
+      "query_type": "filtered",
+      "tau": -0.08496732026143791,
+      "details": {
+        "concordant": 70,
+        "discordant": 83,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "process"
+    },
+    {
+      "query_id": "q-single-00116",
+      "query_type": "single_term",
+      "tau": 0.005847953216374269,
+      "details": {
+        "concordant": 86,
+        "discordant": 85,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "experiment"
+    },
+    {
+      "query_id": "q-multi-01019",
+      "query_type": "multi_term",
+      "tau": 0.3523809523809524,
+      "details": {
+        "concordant": 71,
+        "discordant": 34,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "analysis approach strategy"
+    },
+    {
+      "query_id": "q-common-00856",
+      "query_type": "common_term",
+      "tau": 0.02857142857142857,
+      "details": {
+        "concordant": 108,
+        "discordant": 102,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "network"
+    },
+    {
+      "query_id": "q-single-01264",
+      "query_type": "single_term",
+      "tau": 0.01904761904761905,
+      "details": {
+        "concordant": 107,
+        "discordant": 103,
+        "total_pairs": 210,
+        "common_docs": 21,
+        "only_in_r1": 79,
+        "only_in_r2": 79
+      },
+      "query": "growth"
+    },
+    {
+      "query_id": "q-common-00188",
+      "query_type": "common_term",
+      "tau": 0.0989010989010989,
+      "details": {
+        "concordant": 50,
+        "discordant": 41,
+        "total_pairs": 91,
+        "common_docs": 14,
+        "only_in_r1": 86,
+        "only_in_r2": 86
+      },
+      "query": "database"
+    },
+    {
+      "query_id": "q-filter-00537",
+      "query_type": "filtered",
+      "tau": -0.06432748538011696,
+      "details": {
+        "concordant": 80,
+        "discordant": 91,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "clinical"
+    },
+    {
+      "query_id": "q-rare-01264",
+      "query_type": "rare_term",
+      "tau": 0.2,
+      "details": {
+        "concordant": 63,
+        "discordant": 42,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "framework"
+    },
+    {
+      "query_id": "q-filter-00377",
+      "query_type": "filtered",
+      "tau": 0.09941520467836257,
+      "details": {
+        "concordant": 94,
+        "discordant": 77,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "investment"
+    },
+    {
+      "query_id": "q-common-00240",
+      "query_type": "common_term",
+      "tau": -0.009523809523809525,
+      "details": {
+        "concordant": 52,
+        "discordant": 53,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "cloud"
+    },
+    {
+      "query_id": "q-rare-00612",
+      "query_type": "rare_term",
+      "tau": 0.1695906432748538,
+      "details": {
+        "concordant": 100,
+        "discordant": 71,
+        "total_pairs": 171,
+        "common_docs": 19,
+        "only_in_r1": 81,
+        "only_in_r2": 81
+      },
+      "query": "design"
+    },
+    {
+      "query_id": "q-multi-01445",
+      "query_type": "multi_term",
+      "tau": 0.010526315789473684,
+      "details": {
+        "concordant": 96,
+        "discordant": 94,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "market wellness design diagnosis"
+    },
+    {
+      "query_id": "q-filter-00688",
+      "query_type": "filtered",
+      "tau": -0.031578947368421054,
+      "details": {
+        "concordant": 92,
+        "discordant": 98,
+        "total_pairs": 190,
+        "common_docs": 20,
+        "only_in_r1": 80,
+        "only_in_r2": 80
+      },
+      "query": "theory"
+    },
+    {
+      "query_id": "q-multi-00171",
+      "query_type": "multi_term",
+      "tau": -0.09803921568627451,
+      "details": {
+        "concordant": 69,
+        "discordant": 84,
+        "total_pairs": 153,
+        "common_docs": 18,
+        "only_in_r1": 82,
+        "only_in_r2": 82
+      },
+      "query": "cloud experiment"
+    },
+    {
+      "query_id": "q-single-02340",
+      "query_type": "single_term",
+      "tau": 0.23333333333333334,
+      "details": {
+        "concordant": 74,
+        "discordant": 46,
+        "total_pairs": 120,
+        "common_docs": 16,
+        "only_in_r1": 84,
+        "only_in_r2": 84
+      },
+      "query": "solution"
+    },
+    {
+      "query_id": "q-rare-00703",
+      "query_type": "rare_term",
+      "tau": 0.25,
+      "details": {
+        "concordant": 85,
+        "discordant": 51,
+        "total_pairs": 136,
+        "common_docs": 17,
+        "only_in_r1": 83,
+        "only_in_r2": 83
+      },
+      "query": "implementation"
+    },
+    {
+      "query_id": "q-multi-01984",
+      "query_type": "multi_term",
+      "tau": 0.2571428571428571,
+      "details": {
+        "concordant": 66,
+        "discordant": 39,
+        "total_pairs": 105,
+        "common_docs": 15,
+        "only_in_r1": 85,
+        "only_in_r2": 85
+      },
+      "query": "model solution"
+    },
+    {
+      "query_id": "q-multi-00143",
+      "query_type": "multi_term",
+      "tau": 0.2,
+      "details": {
+        "concordant": 72,
+        "discordant": 48,
+        "total_pairs": 120,
+        "common_docs": 16,
+        "only_in_r1": 84,
+        "only_in_r2": 84
+      },
+      "query": "trading medicine"
+    },
+    {
+      "query_id": "q-filter-00517",
+      "query_type": "filtered",
+      "tau": 0.45054945054945056,
+      "details": {
+        "concordant": 66,
+        "discordant": 25,
+        "total_pairs": 91,
+        "common_docs": 14,
+        "only_in_r1": 86,
+        "only_in_r2": 86
+      },
+      "query": "discovery"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/tests/benches/score-comparability/simulate.py b/tests/benches/score-comparability/simulate.py
index 1541962..7afb0ad 100755
--- a/tests/benches/score-comparability/simulate.py
+++ b/tests/benches/score-comparability/simulate.py
@@ -138,30 +138,59 @@ def score_document_bm25(
     return score
 
 
-def simulate_search(
-    docs: List[Dict],
+def build_inverted_index(docs: List[Dict]) -> Dict[str, List[Tuple[int, Dict]]]:
+    """Build inverted index: term -> [(doc_index, doc), ...]."""
+    index: Dict[str, List[Tuple[int, Dict]]] = defaultdict(list)
+    for i, doc in enumerate(docs):
+        text = f"{doc['title']} {doc['content']}".lower()
+        terms = set(text.split())
+        for term in terms:
+            index[term].append((i, doc))
+    return dict(index)
+
+
+def _collect_candidates(
+    inv_index: Dict[str, List[Tuple[int, Dict]]],
+    doc_categories: List[str],
+    query_terms: Set[str],
+    category_filter: str | None,
+) -> List[Dict]:
+    """Collect unique candidate documents from inverted index."""
+    seen: Set[int] = set()
+    candidates = []
+    for term in query_terms:
+        if term not in inv_index:
+            continue
+        for doc_idx, doc in inv_index[term]:
+            if doc_idx in seen:
+                continue
+            if category_filter and doc_categories[doc_idx] != category_filter:
+                continue
+            seen.add(doc_idx)
+            candidates.append(doc)
+    return candidates
+
+
+def simulate_search_indexed(
+    inv_index: Dict[str, List[Tuple[int, Dict]]],
+    doc_categories: List[str],
     query: Dict,
     stats: Tuple[Dict, int, float],
     limit: int = 100,
 ) -> Dict:
-    """Simulate search on a single index/shard."""
+    """Simulate search using inverted index for fast lookup."""
     df, N, avgdl = stats
     query_terms = tokenize(query["q"])
+    category_filter = query["filter"].split("=")[1].strip() if query.get("filter") else None
+
+    candidates = _collect_candidates(inv_index, doc_categories, query_terms, category_filter)
 
     scores = []
-    for doc in docs:
-        # Apply filter if present
-        if query.get("filter"):
-            category_filter = query["filter"].split("=")[1].strip()
-            if doc["category"] != category_filter:
-                continue
-
+    for doc in candidates:
         score = score_document_bm25(doc, query_terms, df, N, avgdl)
-
         if score > 0:
             scores.append((doc, score))
 
-    # Sort by score descending
     scores.sort(key=lambda x: x[1], reverse=True)
 
     hits = []
@@ -182,47 +211,34 @@ def simulate_search(
     }
 
 
-def simulate_distributed_search(
-    shards: Dict[int, List[Dict]],
+def simulate_distributed_search_indexed(
+    shard_indexes: Dict[int, Dict[str, List[Tuple[int, Dict]]]],
+    shard_doc_categories: Dict[int, List[str]],
     shard_stats: Dict[int, Tuple[Dict, int, float]],
     query: Dict,
     limit: int = 100,
 ) -> Dict:
-    """
-    Simulate distributed search across shards.
-
-    This is where the score comparability issue manifests:
-    - Each shard computes scores using LOCAL statistics
-    - The merger combines results assuming scores are comparable
-    - When shards have different document distributions, this breaks
-    """
+    """Distributed search with score-based merge (the problematic approach)."""
     query_terms = tokenize(query["q"])
-
-    # Collect top results from each shard
-    # Each shard returns offset + limit results (we use limit * 2 for safety)
+    category_filter = query["filter"].split("=")[1].strip() if query.get("filter") else None
     per_shard_limit = limit * 2
     all_hits = []
 
-    for shard_id, docs in shards.items():
+    for shard_id, inv_index in shard_indexes.items():
         df, N, avgdl = shard_stats[shard_id]
+        doc_cats = shard_doc_categories[shard_id]
+        candidates = _collect_candidates(inv_index, doc_cats, query_terms, category_filter)
 
-        # Apply filter at shard level
-        if query.get("filter"):
-            category_filter = query["filter"].split("=")[1].strip()
-            filtered_docs = [d for d in docs if d["category"] == category_filter]
-        else:
-            filtered_docs = docs
-
-        scores = []
-        for doc in filtered_docs:
+        shard_scores = []
+        for doc in candidates:
             score = score_document_bm25(doc, query_terms, df, N, avgdl)
             if score > 0:
-                scores.append((doc, score, shard_id))
+                shard_scores.append((doc, score))
 
-        scores.sort(key=lambda x: x[1], reverse=True)
-        all_hits.extend(scores[:per_shard_limit])
+        shard_scores.sort(key=lambda x: x[1], reverse=True)
+        for doc, score in shard_scores[:per_shard_limit]:
+            all_hits.append((doc, score, shard_id))
 
-    # Merge: global sort by score (ASSUMING SCORES ARE COMPARABLE)
     all_hits.sort(key=lambda x: x[1], reverse=True)
 
     hits = []
@@ -241,60 +257,48 @@ def simulate_distributed_search(
         "filter": query.get("filter"),
         "hits": hits,
         "total_hits": len(all_hits),
-        "shards_queried": list(shards.keys()),
+        "shards_queried": list(shard_indexes.keys()),
     }
 
 
 RRF_K = 60  # RRF constant, matching merger.rs
 
 
-def simulate_distributed_search_rrf(
-    shards: Dict[int, List[Dict]],
+def simulate_distributed_search_rrf_indexed(
+    shard_indexes: Dict[int, Dict[str, List[Tuple[int, Dict]]]],
+    shard_doc_categories: Dict[int, List[str]],
     shard_stats: Dict[int, Tuple[Dict, int, float]],
     query: Dict,
     limit: int = 100,
 ) -> Dict:
-    """
-    Simulate distributed search using Reciprocal Rank Fusion.
-
-    RRF score for a document: sum over shards of 1/(k + rank + 1)
-    where rank is 0-based position in shard's result list.
-
-    This avoids the score comparability issue entirely because
-    RRF only uses rank position, not raw scores.
-    """
+    """Distributed search using Reciprocal Rank Fusion."""
     query_terms = tokenize(query["q"])
+    category_filter = query["filter"].split("=")[1].strip() if query.get("filter") else None
     per_shard_limit = limit * 2
 
-    # Accumulate RRF scores per document
     rrf_scores: Dict[str, float] = defaultdict(float)
-    doc_info: Dict[str, Tuple[Dict, int]] = {}  # id -> (doc, shard_id)
+    doc_info: Dict[str, Tuple[Dict, int]] = {}
 
-    for shard_id, docs in shards.items():
+    for shard_id, inv_index in shard_indexes.items():
         df, N, avgdl = shard_stats[shard_id]
+        doc_cats = shard_doc_categories[shard_id]
+        candidates = _collect_candidates(inv_index, doc_cats, query_terms, category_filter)
 
-        if query.get("filter"):
-            category_filter = query["filter"].split("=")[1].strip()
-            filtered_docs = [d for d in docs if d["category"] == category_filter]
-        else:
-            filtered_docs = docs
-
-        scores = []
-        for doc in filtered_docs:
+        shard_scores = []
+        for doc in candidates:
             score = score_document_bm25(doc, query_terms, df, N, avgdl)
             if score > 0:
-                scores.append((doc, score))
+                shard_scores.append((doc, score))
 
-        scores.sort(key=lambda x: x[1], reverse=True)
+        shard_scores.sort(key=lambda x: x[1], reverse=True)
 
-        for rank, (doc, _score) in enumerate(scores[:per_shard_limit]):
+        for rank, (doc, _score) in enumerate(shard_scores[:per_shard_limit]):
             doc_id = doc["id"]
             rrf_contribution = 1.0 / (RRF_K + rank + 1)
             rrf_scores[doc_id] += rrf_contribution
             if doc_id not in doc_info:
                 doc_info[doc_id] = (doc, shard_id)
 
-    # Sort by RRF score descending
     sorted_docs = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
 
     hits = []
@@ -314,7 +318,7 @@ def simulate_distributed_search_rrf(
         "filter": query.get("filter"),
         "hits": hits,
         "total_hits": len(sorted_docs),
-        "shards_queried": list(shards.keys()),
+        "shards_queried": list(shard_indexes.keys()),
         "merge_strategy": "rrf",
     }
 
@@ -362,6 +366,19 @@ def run_experiment(
     queries = load_queries(query_file)
     print(f"  {len(queries)} queries")
 
+    # Build inverted indexes for fast lookup
+    print("\nBuilding inverted indexes...")
+    global_inv_index = build_inverted_index(docs)
+    global_doc_categories = [doc.get("category", "") for doc in docs]
+    print(f"  Global index: {len(global_inv_index)} terms")
+
+    shard_indexes = {}
+    shard_doc_categories = {}
+    for shard_id, shard_docs in shards.items():
+        shard_indexes[shard_id] = build_inverted_index(shard_docs)
+        shard_doc_categories[shard_id] = [d.get("category", "") for d in shard_docs]
+        print(f"  Shard {shard_id}: {len(shard_indexes[shard_id])} terms")
+
     # Run experiments
     output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -379,18 +396,23 @@ def run_experiment(
                 print(f"  Processed {i + 1} queries...")
 
             # Ground truth: single index with global statistics
-            gt_result = simulate_search(docs, query, global_stats, limit)
+            gt_result = simulate_search_indexed(
+                global_inv_index, global_doc_categories,
+                query, global_stats, limit,
+            )
             gt_f.write(json.dumps(gt_result) + "\n")
 
             # Distributed: each shard uses local statistics (score-based merge)
-            dist_result = simulate_distributed_search(
-                shards, shard_stats, query, limit
+            dist_result = simulate_distributed_search_indexed(
+                shard_indexes, shard_doc_categories,
+                shard_stats, query, limit,
             )
             dist_f.write(json.dumps(dist_result) + "\n")
 
             # RRF: rank-based merge (no score comparability needed)
-            rrf_result = simulate_distributed_search_rrf(
-                shards, shard_stats, query, limit
+            rrf_result = simulate_distributed_search_rrf_indexed(
+                shard_indexes, shard_doc_categories,
+                shard_stats, query, limit,
             )
             rrf_f.write(json.dumps(rrf_result) + "\n")