  Proving geometric theorems constitutes a hallmark of visual reasoning
combining both intuitive and logical skills. Therefore, automated theorem
proving of Olympiad-level geometry problems is considered a notable milestone
in human-level automated reasoning. The introduction of AlphaGeometry, a
neuro-symbolic model trained with 100 million synthetic samples, marked a major
breakthrough. It solved 25 of 30 International Mathematical Olympiad (IMO)
problems whereas the reported baseline based on Wu's method solved only ten. In
this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry,
and find that Wu's method is surprisingly strong. Wu's method alone can solve
15 problems, and some of them are not solved by any of the other methods. This
leads to two key findings: (i) Combining Wu's method with the classic synthetic
methods of deductive databases and angle, ratio, and distance chasing solves 21
out of 30 methods by just using a CPU-only laptop with a time limit of 5
minutes per problem. Essentially, this classic method solves just 4 problems
less than AlphaGeometry and establishes the first fully symbolic baseline
strong enough to rival the performance of an IMO silver medalist. (ii) Wu's
method even solves 2 of the 5 problems that AlphaGeometry failed to solve.
Thus, by combining AlphaGeometry with Wu's method we set a new state-of-the-art
for automated theorem proving on IMO-AG-30, solving 27 out of 30 problems, the
first AI method which outperforms an IMO gold medalist.
