diff --git a/README.md b/README.md
index 053e970..fe4d4cb 100644
--- a/README.md
+++ b/README.md
@@ -218,9 +218,24 @@ and finally the artist itself. Since most requests are answered by the first
 query, the extra searches cost nothing in the common case — `MUSE_MAX_QUERIES`
 caps how far it will go.
 
-Dropping the artist from the *query* does not mean ignoring it. The artist is
-then matched against the whole **folder path**, which is where a well-organised
-share puts it anyway:
+Dropping the artist from the *query* does not mean ignoring it — it makes the
+artist matter more. A bare-title search stops returning "this song from various
+peers" and starts returning *every song anyone ever called Chicago*: The Fall,
+Groove Armada, Sufjan Stevens. The artist is then the only thing separating the
+right recording from a different song entirely, so it is a **gate**: candidates
+whose path names the artist are preferred outright, and ones that do not are
+used only when nothing else is left.
+
+That has to be a gate rather than a weighting, and the reason is worth stating.
+A perfect title with no artist scores 0.75, comfortably above `MUSE_MIN_MATCH`,
+so as a mere contribution it loses to availability — and an idle peer holding
+the wrong song beats a busy one holding the right song. That is not
+hypothetical either: it filed The Fall's "Chicago, Now!" as Michael Jackson.
+The same principle the confidence score is built on applies here: a fast peer
+must not be able to promote a wrong match.
+
+The artist is matched against the whole **folder path**, which is where a
+well-organised share puts it anyway:
 
 ```
 anjya music/Michael Jackson/1982 - Thriller [flac]/06 - Billie Jean.flac
diff --git a/app.py b/app.py
index eab156a..c1a0507 100644
--- a/app.py
+++ b/app.py
@@ -481,6 +481,30 @@ def similarity(want: str, got: str) -> float:
     return max(seq, len(a & b) / len(a) if a else 0.0)
 
 
+def artist_confirmed(remote_path: str, artist: str) -> bool:
+    """Is the requested artist actually named somewhere in this path?
+
+    This is a gate rather than a score because of what the query ladder does.
+    Once a filtered artist name forces the search down to the bare title, the
+    result set stops being "this song from various peers" and becomes "every
+    song anyone ever called Chicago" -- The Fall, Groove Armada, Sufjan
+    Stevens. The artist is then the only thing distinguishing the right
+    recording from a completely different song, and a weighted contribution is
+    not enough: 0.75 for a perfect title with no artist still clears MIN_MATCH,
+    and an available stranger will out-score a queued match every time.
+    """
+    na = norm(artist)
+    if not na:
+        return False
+    path = norm(remote_path.replace("\\", "/"))
+    if na in path:
+        return True
+    # Every distinctive word, in any order: catches "Jackson, Michael" and
+    # "MJ - Chicago (Michael Jackson)" without matching on "the" or "and".
+    words = [w for w in na.split() if len(w) > 3]
+    return bool(words) and all(w in path for w in words)
+
+
 def match_ratio(remote_path: str, artist: str, title: str) -> float:
     """How well a remote path matches the track asked for, 0..1.
 
@@ -1078,6 +1102,20 @@ async def pick_and_enqueue(job_id: str, artist: str, title: str,
         set_state(job_id, "failed", no_candidate_detail(seen, stats, queries))
         return
 
+    # Prefer candidates whose path actually names the artist, and only settle
+    # for ones that do not when there is nothing else. Availability is a
+    # tiebreak among plausible files, never a reason to take the wrong song.
+    named = {k: v for k, v in pool.items()
+             if artist_confirmed(v[2]["filename"], artist)}
+    if named:
+        if len(named) < len(pool):
+            log.info("job %s keeping %d/%d candidates that name %r",
+                     job_id[:8], len(named), len(pool), artist)
+        pool = named
+    else:
+        log.info("job %s no candidate names %r; falling back to title-only "
+                 "matches, which review will have to judge", job_id[:8], artist)
+
     ranked = sorted(pool.values(), key=lambda r: -r[0])
     cands = spread_by_peer(ranked)[:CANDIDATES]
     with db() as c:
diff --git a/tests/test_acquisition.py b/tests/test_acquisition.py
index feebae4..27587f1 100644
--- a/tests/test_acquisition.py
+++ b/tests/test_acquisition.py
@@ -161,6 +161,35 @@ def main() -> int:
     check("reordered filename still matches",
           app.match_ratio(r"x\Creep - Radiohead.mp3", "Radiohead", "Creep") == 1.0)
 
+    print("\nthe artist gate")
+    check("artist named in the folder is confirmed",
+          app.artist_confirmed(r"m\Michael Jackson\Xscape\02 Chicago.flac",
+                               "Michael Jackson"))
+    check("a different band's song with the same title is not",
+          not app.artist_confirmed(
+              r"@@jwbvv\Music\The Fall\1990-Extricate\08-Chicago, Now!.flac",
+              "Michael Jackson"))
+    check("out-of-order naming still counts",
+          app.artist_confirmed(r"x\Jackson, Michael - Chicago.flac",
+                               "Michael Jackson"))
+
+    # The real regression: bare-title search returns every song called
+    # "Chicago", and an available stranger used to outrank a queued match.
+    gate = FakeSlskd(per_query={"Chicago": [
+        peer("stranger", r"@@x\Music\The Fall\1990-Extricate\08-Chicago, Now!.flac",
+             slot=True, queue=0),
+        peer("correct", r"m\Michael Jackson\XSCAPE\02 - Chicago.flac",
+             slot=False, queue=300),
+    ]})
+    app.slskd_client = gate
+    make_job("j6", "Michael Jackson", "Chicago")
+    asyncio.run(app.pick_and_enqueue("j6", "Michael Jackson", "Chicago"))
+    r = job_row("j6")
+    check("the right artist wins even from a peer with 300 queued",
+          r["username"] == "correct", f"picked {r['username']}")
+    check("and the wrong-artist file is dropped, not merely ranked lower",
+          len(json.loads(r["candidates"] or "[]")) == 0, r["candidates"])
+
     print("\ncandidate ladder")
     fake = FakeSlskd(per_query={
         "Billie Jean": [peer("alice", r"m\Michael Jackson\Billie Jean.flac"),