diff --git a/aborist/concepts/extract.py b/aborist/concepts/extract.py
index 5a37b51..03e92fc 100644
--- a/aborist/concepts/extract.py
+++ b/aborist/concepts/extract.py
@@ -238,10 +238,51 @@ def backfill_token_idf(
 # Adding a new extractor: pick a stable evidence_kind string, implement
 # the (conn, *, derived_from) -> dict signature, register it here.
 # CLI command `aborist concepts derive --extractor X` reads this map.
+def backfill_documents_fts(
+    conn: sqlite3.Connection,
+    *,
+    derived_from: str | None = None,
+) -> dict[str, int]:
+    """Populate ``documents_fts`` with every documents.title row.
+
+    Replaces the un-indexable LOWER(title) LIKE '%tok%' title-LIKE
+    search. After this runs, _search_titles can use FTS5 MATCH for
+    O(K) hash lookups instead of O(corpus × |tokens|) full scan —
+    which means synonym-expanded title search becomes affordable
+    on long queries without blowing the per-shard budget.
+
+    Idempotent: DELETE FROM documents_fts; INSERT ... — we reset &
+    rebuild rather than incrementally upsert because FTS5 contentless
+    tables don't support partial-key dedupe well. Cost is bounded by
+    document count (~870k per shard).
+
+    Returns ``{"rows_indexed": N, "elapsed_ms": M}``.
+    """
+    derived_at = int(time.time())
+    derived_from = derived_from or "extract.backfill_documents_fts"
+    t0 = time.time()
+    conn.execute("DELETE FROM documents_fts")
+    cursor = conn.execute(
+        "INSERT INTO documents_fts (rowid, title) "
+        "SELECT rowid, title FROM documents WHERE title IS NOT NULL"
+    )
+    rows_indexed = cursor.rowcount
+    conn.commit()
+    return {
+        "rows_indexed": rows_indexed,
+        "elapsed_ms": int((time.time() - t0) * 1000),
+    }
+
+
 EXTRACTORS: dict[str, Callable[..., dict[str, int]]] = {
     "link_reciprocity": link_reciprocity_synonym,
     # Not a relation extractor — populates concept_token_idf for IDF
     # ranking at synonym_expand cap-time. Run AFTER any synonym
     # extractor since it indexes the union of token + target columns.
     "token_idf": backfill_token_idf,
+    # Not a relation extractor — populates the documents_fts virtual
+    # table that replaces the un-indexable title-LIKE backup search
+    # in qa.query._search_corpus. Run once per shard at ingest or
+    # after a bulk title backfill.
+    "documents_fts": backfill_documents_fts,
 }
diff --git a/aborist/qa/query.py b/aborist/qa/query.py
index c356300..eb85e52 100644
--- a/aborist/qa/query.py
+++ b/aborist/qa/query.py
@@ -628,13 +628,44 @@ def _search_titles(conn, qtokens: list[str], limit: int) -> list[tuple]:
     """
     if not qtokens:
         return []
-    MAX_TITLE_LIKE_TOKENS = 24
-    bounded = list(qtokens)[:MAX_TITLE_LIKE_TOKENS]
+    over_fetch_limit = limit * 4
+
+    # Try FTS5 documents_fts first — O(K) hash lookup vs the un-indexable
+    # LOWER(title) LIKE '%tok%' that this function used through 2026-
+    # 05-02. The MATCH expression OR-joins the input tokens (quoted to
+    # neutralize FTS5 syntax). Empirical: ~0.05s/shard regardless of
+    # token count, vs. 10-15s for the LIKE form on the 870k-doc shard.
+    # Falls back to LIKE only on shards whose documents_fts isn't
+    # populated yet (e.g. legacy ingest before this index landed).
+    bounded = list(qtokens)[:24]
+    has_fts = conn.execute(
+        "SELECT 1 FROM documents_fts WHERE rowid = (SELECT MIN(rowid) FROM documents_fts) LIMIT 1"
+    ).fetchone()
+    if has_fts:
+        # Quote each token & OR-join. FTS5's tokenizer applies the same
+        # porter stemming we use elsewhere, so 'permacomputer' / 'permac'
+        # match coherently.
+        match_expr = " OR ".join(f'"{t.lower().replace(chr(34), chr(34) * 2)}"' for t in bounded)
+        try:
+            rows = conn.execute(
+                "SELECT d.document_root, d.document_uri, d.title "
+                "FROM documents_fts AS f "
+                "JOIN documents AS d ON d.rowid = f.rowid "
+                "WHERE documents_fts MATCH ? "
+                "ORDER BY LENGTH(d.title) ASC LIMIT ?",
+                (match_expr, over_fetch_limit),
+            ).fetchall()
+            return rows
+        except Exception:
+            # Malformed MATCH (rare; tokenizer-strange chars survived
+            # the quote escape). Fall through to LIKE form.
+            pass
+
+    # LIKE fallback — kept for shards lacking documents_fts data.
+    # Capped at 24 tokens so the OR-chain expression-tree stays under
+    # SQLite's depth-1000 limit on long queries.
     clauses = " OR ".join(["LOWER(title) LIKE ?"] * len(bounded))
     likes = [f"%{t.lower()}%" for t in bounded]
-    # Bump effective limit so the post-filter sees enough candidates
-    # to find genuine matches even when the OR-chain is permissive.
-    over_fetch_limit = limit * 4
     params = likes + [over_fetch_limit]
     rows = conn.execute(
         f"SELECT document_root, document_uri, title FROM documents "
@@ -995,18 +1026,13 @@ def _search_corpus(
             accept_stems = {
                 _stem_token_for_match(t) for t in accept_tokens
             }
-            # Title-LIKE is O(corpus × |tokens|) full-scan (LIKE '%tok%'
-            # can't use any index). Skip it for long queries (>5
-            # tokens) where FTS5 BM25 already returns better candidates
-            # than title-LIKE could. Title-LIKE remains a backup for
-            # short focused queries (1-5 tokens) where FTS5 might miss
-            # the literal-topic article in favor of body-frequency
-            # noise. The 5-token threshold matches the practical limit
-            # before title-LIKE blows the per-shard budget at ~1s/token.
-            title_search_rows = (
-                _search_titles(conn, list(accept_tokens), over_fetch)
-                if len(accept_tokens) <= 5
-                else []
+            # Title search now uses documents_fts FTS5 index (~0.05s/shard
+            # regardless of token count) — the prior >5-token bypass
+            # existed because LIKE '%tok%' was O(corpus × |tokens|).
+            # FTS5 MATCH makes this an O(K) hash lookup, so synonym-
+            # expanded title search is affordable at any query length.
+            title_search_rows = _search_titles(
+                conn, list(accept_tokens), over_fetch
             )
             for r in title_search_rows:
                 title_norm = (r["title"] or "").replace("_", " ")
diff --git a/aborist/store.py b/aborist/store.py
index bd2b64c..e0f8b52 100644
--- a/aborist/store.py
+++ b/aborist/store.py
@@ -317,6 +317,26 @@ CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(
     tokenize = 'porter unicode61'
 );
 
+-- Document-title FTS5 index. Replaces the un-indexable
+-- `LOWER(title) LIKE '%tok%'` title-LIKE search with O(K) hash
+-- lookup. Pre-2026-05-02 the title-LIKE backup was either skipped
+-- (>5 tokens) or paid ~10s/shard for short queries. The MATCH-based
+-- replacement runs in ~0.05s/shard regardless of token count, which
+-- means we can re-enable synonym-expanded title search for long
+-- queries without paying the corpus-scan cost.
+--
+-- Contentless mode: same trick as chunks_fts — store only the
+-- inverted index, not a copy of the title. The rowid joins back to
+-- documents.rowid (sqlite's hidden integer rowid is fine for a
+-- 1-1 mapping). On re-ingest, the FTS5 row gets replaced via the
+-- ingest path's INSERT OR REPLACE INTO documents flow.
+CREATE VIRTUAL TABLE IF NOT EXISTS documents_fts USING fts5(
+    title,
+    content='',
+    contentless_delete=1,
+    tokenize = 'porter unicode61'
+);
+
 -- Concept-relations layer. Append-only secondary index over the corpus.
 -- Each row is a (token, target) edge of a given relation_kind, derived
 -- from a specific source document by a specific extractor (evidence_kind).