We found that the performance gap varied substantially across the data sets for some splitters compared to others. For example, for the scaffold splitter, the mean gap in ∆ ROC-AUC ranged from 0.01 (CYP2C9, CYP2C19, CYP2D6, CYP3A4) to 0.04 (HERG), whereas for the max dissimilarity splitter, the gap was wider, ranging from -0.05 (CYP1A2) to 0.12 (HERG). To determine to what extent these observations are model-dependent, we calculated these metrics for classical ML and GNN models (Table and Figure ). One observation is that whenever the performance gap increased for classical ML models, we saw a corresponding increase in performance for the GNN models. This indicates that both types of models follow the same pattern.