Computational and machine learning (ML)-based approaches to predicting binding affinity are critical research directions in drug discovery. A strong predictor of a ligand affinity is desirable both for hit identification in virtual screening and for computationally evaluating structure-activity relationships for hit expansion and hit-to-lead optimization. These approaches include ligand-based methods that exclusively use 2-dimensional (2D) molecular representations of known ligands to infer binding based on molecular similarity and structure-based approaches that encode three-dimensional (3D) protein-ligand interactions. Despite recent advances in these approaches, predicting ligand affinity remains a critical challenge in computational drug design, particularly for generalizing to novel chemotypes. Developing strong computational predictors would enable the computationally assisted medicinal chemist to evaluate many more compounds in less time and cost than a purely experimental approach.