First, the number of data points (ID vs OOD ROC-AUC) is insufficient for some datasetsplitting combinations (90 for GNN models and 30 for classical ML models) to establish reliable linear relationships. Second, the narrow dynamic range of ID performance (between minimum and maximum ID ROC-AUC; classical ML: 0.056; GNNs: 0.078 averaged over the data sets) means prediction noise can dominate the observed patterns. Third, some data sets actually exhibit inverse ID-OOD correlations, which is in contradiction with the consensus trend. These cases need further investigation, particularly with larger sample sizes (more trained models) and a broader performance dynamic range.