diff --git a/graphrag_sdk/src/graphrag_sdk/retrieval/strategies/chunk_retrieval.py b/graphrag_sdk/src/graphrag_sdk/retrieval/strategies/chunk_retrieval.py
index 3f42fcaa..e71e4455 100644
--- a/graphrag_sdk/src/graphrag_sdk/retrieval/strategies/chunk_retrieval.py
+++ b/graphrag_sdk/src/graphrag_sdk/retrieval/strategies/chunk_retrieval.py
@@ -51,17 +51,25 @@ def _add(cid: str, text: str, source: str) -> None:
     except Exception as exc:
         logger.debug("Chunk vector search failed: %s", exc)
 
-    # Path C: MENTIONED_IN — 3 chunks per entity (batched UNWIND)
+    # Path C: MENTIONED_IN — top-3 chunks per entity, ranked by cosine
+    # distance to the query embedding. Hub entities (e.g. the main
+    # product name) can be MENTIONED_IN hundreds of chunks; the previous
+    # COLLECT(c)[..3] picked an arbitrary 3, almost never including the
+    # chunks most relevant to the current query. Ranking by cosine here
+    # surfaces the chunks closest to the query intent.
     eids_mention = [eid for eid, _ in entity_list[:15]]
     if eids_mention:
         try:
             result = await graph_store.query_raw(
                 "UNWIND $eids AS eid "
                 "MATCH (e:__Entity__ {id: eid})-[:MENTIONED_IN]->(c:Chunk) "
+                "WHERE c.embedding IS NOT NULL "
+                "WITH eid, c, vec.cosineDistance(c.embedding, vecf32($qv)) AS dist "
+                "ORDER BY eid, dist ASC "
                 "WITH eid, COLLECT(c)[..3] AS chunks "
                 "UNWIND chunks AS c "
                 "RETURN eid, c.id AS id, c.text AS text",
-                {"eids": eids_mention},
+                {"eids": eids_mention, "qv": query_vector},
             )
             for row in result.result_set:
                 cid = row[1]
diff --git a/graphrag_sdk/tests/test_multi_path_retrieval.py b/graphrag_sdk/tests/test_multi_path_retrieval.py
index 53092946..b535730e 100644
--- a/graphrag_sdk/tests/test_multi_path_retrieval.py
+++ b/graphrag_sdk/tests/test_multi_path_retrieval.py
@@ -233,6 +233,55 @@ async def capture_query(cypher, params=None):
         ]
         assert len(twohop_chunk_queries) >= 1
 
+    async def test_mentioned_in_ranks_chunks_by_cosine(self, mp_graph_store, mp_vector_store, mp_embedder, mp_llm):
+        """The MENTIONED_IN path must rank chunks per entity by cosine
+        distance to the query embedding — not pick an arbitrary 3.
+
+        Hub entities can be MENTIONED_IN hundreds of chunks; arbitrary
+        selection almost never surfaces the chunks relevant to the
+        current query (regression: see PR referencing issue #258).
+        """
+        # Seed entity discovery so Path C runs (mirrors the setup in
+        # ``test_mentioned_in_and_2hop_chunk_paths``).
+        mp_vector_store.search_relationships = AsyncMock(return_value=[
+            {"src_name": "Alice", "type": "WORKS_AT", "tgt_name": "Acme", "fact": "engineer", "score": 0.9},
+        ])
+
+        captured: list[tuple[str, dict]] = []
+
+        async def capture_query(cypher, params=None):
+            captured.append((cypher, params or {}))
+            result = MagicMock()
+            result.result_set = []
+            return result
+
+        mp_graph_store.query_raw = AsyncMock(side_effect=capture_query)
+
+        s = MultiPathRetrieval(
+            graph_store=mp_graph_store,
+            vector_store=mp_vector_store,
+            embedder=mp_embedder,
+            llm=mp_llm,
+        )
+        await s.search("Who is Alice?")
+
+        # Find the direct MENTIONED_IN query (entity -> chunk, not 2-hop)
+        direct_mention = [
+            (q, p) for q, p in captured
+            if "MENTIONED_IN" in q and "Chunk" in q and "neighbor" not in q.lower()
+        ]
+        assert direct_mention, "expected at least one direct MENTIONED_IN chunk query"
+
+        cypher, params = direct_mention[0]
+        # The fix: rank by cosine distance to the query vector before
+        # COLLECT, so per-entity chunk selection is query-relevant.
+        assert "vec.cosineDistance" in cypher, (
+            "MENTIONED_IN chunk query must rank by cosine distance to the "
+            "query vector (regression of issue #258)"
+        )
+        assert "ORDER BY" in cypher, "expected ORDER BY to make COLLECT[..3] meaningful"
+        assert "qv" in params, "query vector must be passed as a parameter"
+
     async def test_format_produces_sections(self, mp_graph_store, mp_vector_store, mp_embedder, mp_llm):
         """Output should include structured sections when data is available."""
         mp_vector_store.search_relationships = AsyncMock(return_value=[