One approach to control the molecular composition bias in ligand selection for model training is to use ligand representations that do not rely directly on molecular composition, but instead on physical interactions between the ligand and protein. Examples of such representations are interaction energies computed using molecular mechanics force fields (MDenerg) or protein-ligand interaction fingerprints (PLEC). In the current study, however, none of these representations were able to outperform simple 2D_3D ligand based descriptors. More generally, training models on ligand-only information leads to memorisation of ligand features , even across different host proteins . A wishful thought is for the model to learn the underlying physics by providing protein-ligand interaction descriptors as input. In practise, though, doing so hardly improves the accuracies of the models in question , at least not without extensive sampling of the model applicability space in the training set . Furthermore, many lead optimization datasets such as that used here exhibit 2D bias as the molecules were often designed and synthesized iteratively based on underlying 2D chemical similarity principles.