Previous reports suggest there is a strong linear positive correlation between ID and OOD performance, i.e., higher ID leads to higher OOD performance. the accuracy-on-the-line concept, which shows empirically that there is a strong correlation between ID and OOD accuracy for a wide range of models and distribution shift in the computer vision domain. If this relationship holds, it can be used as a proxy for model selection for achieving higher OOD generalization. Since the introduction of the accuracy- on-the-line concept, many other studies have supported or cast doubts on this idea. We investigated the on-the-line concept when applied to the molecular domain, in particular molecular property prediction, to understand to what degree this observation is still valid on data from the chemical domain. First, we investigated to find out if there is a strong correlation (Pearson correlation and R 2 ), between ID and OOD performance for our splitting strategies, models, and data sets. Moreover, we tried to make the connection between the ID-OOD correlation and the hardness of splitting strategies. In the following section, we analyze the ID-OOD correlation for classical ML and GNN models when combining the results, ID and OOD ROC-AUC, for all the splitting strategies and data sets. Furthermore, we performed the same analysis considering each splitting strategy separately (Figure ).