Hit-to-lead optimization in drug discovery projects typically combines the optimization of on-target potency and selectivity against off-targets with achieving suitable absorption, distribution, metabolism, and excretion (ADME) properties . While machine learning models have demonstrated useful prediction accuracy for certain physicochemical properties, such as lipophilicity or permeability, predicting drug potency or protein-ligand complexes proves to be notoriously challenging . Methods employed include physics-based approaches, such as free energy perturbation (FEP) calculations, semi-empirical quantum chemistry approximations, and deep learning, including GNNs . Moreover, ligand-based machine learning was combined with three-dimensional (3D) scoring using template-based docking .