Considering all the splitting strategies and data sets collectively, we observed a strong positive correlation between ID and OOD ROC-AUC for both classical ML (Pearson r = 0.68) and GNN models (Pearson's r = 0.70) (Figure ). However, a more nuanced picture emerged when analyzing individual splitting strategies (Figure ). The correlation strength varied markedly across different splitting strategies. We observed a strong correlation among random and scaffold splitting (classical ML: r=0.97, 0.94; GNNs: r=0.97 and 0.93, respectively), while some other splitters, like K-means and max dissimilarity, showed substantially weaker relationships (classical ML: r=0.40 and 0.34; GNNs: r=0.50 and 0.54, respectively).