We investigated the affinity predictions for ligands that are found in multiple structures in the PDBbind database. We clustered the structures of the PDBbind 2018 general set by the three-character chemical ID of the ligand as specified in the PDB, and selected all ligands found in at least three structures. Holding out each ligand in turn as a test case, we trained a RF regression model using only the RDKit descriptors on all structures not containing that ligand, and used the resulting model to predict the affinity of that ligand. We repeated the above process for each ligand by removing from the training set all ligands with a Tanimoto similarity to any test set ligand above a defined threshold. We then trained a new RF regression model and predicted the affinity of the test ligand.