In the second part of this study, we investigate the connection between ID and OOD performance. While earlier studies suggest a substantial positive correlation between these metrics, implying that ID performance could serve as a proxy for model selection, recent work has challenged this assumption. Our analysis across various data sets and splitting strategies reveals that the relationship between ID and OOD performance can vary significantly, sometimes even showing negative correlations. These findings suggest that model selection strategies concentrating solely on ID performance may not consistently yield optimal generalization capabilities. More nuanced approaches are therefore needed for the evaluation and selection of models in drug discovery applications.