The most interesting case was the TP dataset, where Glide SP was almost flawless, outperforming both PM6/COSMO and SQM-ML. Looking at the retrospectively calculated optimum feature importances, we noticed that for the compounds of TP the Eint had minimal importance, while the optimum score should be dominated mainly by interaction types and to a lesser extent by the AMW and deepFl_logP. This is nicely illustrated in Figure where it can be seen that the worst-scored molecules had small sizes but many chemical groups that facilitated many different types of P-L interactions, which were imprinted on their protein-ligand interaction fingerprints.