Spearman's ρ=0.952 and 0.976; TMDs: Spearman's ρ=0.524 and 0.667, respectively). Given that both Tanimoto distance and classical ML models use the Morgan fingerprint, a strong correlation was expected between their rankings. However, the similarly strong correlation between Tanimoto distance and GNN was unexpected. These findings indicate that Tanimoto distance serves as a better predictor of performance drop across different splitting methods for both classical ML and GNN models compared to TMD.