To run the KNN model (with K=1) on a new protein-ligand pair, we simply find the molecule with the closest S in the BigBind training set (without SNA) and return S as the activity score. We note that, unlike the KNN used by Volkov et al. , this KNN incorporates both ligand and receptor information and is thus a more robust baseline. The architecture of Banana is shown in Figure . For the model input, we prepare graphs for both the receptor binding pocket and the ligand. Following previous studies, the nodes of the receptor graph are the residues, labeled with the amino acid name. An edge exists between two nodes in the receptor graph if their α-carbons are within 20 Å of each other. The scalar distance between them is used as edge data. For the ligand graph, we simply use the molecule graph. The nodes are heavy atoms and are labeled with the element, formal charge, hybridization, number of bonded hydrogens, and whether or not the atom is aromatic. The edges are bonds and are labeled with the bond order.