We observed similar ID-OOD correlation patterns between classical ML and GNN models across different splitting strategies. When a splitting strategy showed a high correlation for classical ML models, it typically showed a comparable correlation for GNN models. Both model types exhibited the strongest correlations with random, scaffold, and scaffold generic splitting (classical ML: r = 0.97, 0.94, and 0.89; GNNs: r = 0.97, 0.93, and 0.89, respectively), and the weakest correlations with K-means and max dissimilarity splitting (classical ML: r = 0.40 and 0.34; GNNs: r = 0.50 and 0.51, respectively).