diff --git a/web/lib/retrieval/hybrid.ts b/web/lib/retrieval/hybrid.ts
index b157868..df16bbb 100644
--- a/web/lib/retrieval/hybrid.ts
+++ b/web/lib/retrieval/hybrid.ts
@@ -85,25 +85,32 @@ export async function hybridSearch(opts: HybridSearchOptions): Promise<ChunkHit[
 
   if (rows.length === 0) return [];
 
-  // 3. Optional rerank
-  if (no_rerank || rows.length <= top_k) {
+  // 3. Rerank — the cross-encoder score is BOTH the relevance ordering AND the
+  // noise filter. The RPC always returns up to recall_k dense neighbours even
+  // when the term has zero lexical match (e.g. a name absent from the corpus),
+  // so without this filter a nonsense query returns its k nearest vectors.
+  // BGE-reranker-v2-m3 with normalize=true → sigmoid 0..1; ~0 means irrelevant.
+  const RELEVANCE_MIN = 0.02;
+
+  if (no_rerank) {
     return rows.slice(0, top_k);
   }
 
-  const candidateTexts = rows.map((r) => {
-    if (lang === "en") return r.content_en || r.content_pt || "";
-    return r.content_pt || r.content_en || "";
-  });
+  const candidateTexts = rows.map((r) =>
+    lang === "en" ? r.content_en || r.content_pt || "" : r.content_pt || r.content_en || "",
+  );
   let scores: number[] = [];
   try {
     scores = await rerank(query, candidateTexts);
   } catch {
-    // Reranker unavailable — return RRF order
+    // Reranker unavailable — fall back to RRF order (no relevance filter possible)
     return rows.slice(0, top_k);
   }
-  const reranked = rows.map((r, i) => ({ ...r, rerank_score: scores[i] }));
-  reranked.sort((a, b) => (b.rerank_score ?? 0) - (a.rerank_score ?? 0));
-  return reranked.slice(0, top_k);
+  return rows
+    .map((r, i) => ({ ...r, rerank_score: scores[i] ?? 0 }))
+    .filter((r) => (r.rerank_score ?? 0) >= RELEVANCE_MIN)
+    .sort((a, b) => (b.rerank_score ?? 0) - (a.rerank_score ?? 0))
+    .slice(0, top_k);
 }
 
 /** Quick chunk lookup by chunk_id (no embedding). For citation expansion. */