To test our reaction informatics approach to map drug space, we selected 1,213 FDA-approved drugs as a representative high diversity set. As examples of a more focused series, we accessed the ChEMBL database and retrieved 1,500 analogs of the small molecule drug afatinib, a kinase inhibitor blocking the endothelial growth factor receptor (EGFR) and used to treat non-small cell lung carcinoma (NSCLC), as well as 274 analogs of polymyxin B (PMB), an FDA-approved macrocyclic peptide natural product considered as a last resort antibiotic against multidrug-resistant bacteria. To represent molecular similarities, we considered three types of molecular fingerprints. First, we selected the classical Morgan fingerprint, 38 also called extended connectivity fingerprint (ECFP), which is a binary fingerprint encoding the presence of specific atom-centered circular substructures up to a diameter of four (ECFP4) and six (ECFP6) bonds, as well as our recently reported MinHashed fingerprint MHFP6, which similarly encodes circular substructures up to a diameter of six bonds using shingling and MinHashing to compress information. These circular substructure fingerprints are particularly efficient in virtual screening benchmarks and off-target prediction tasks. Second, we considered three pharmacophore fingerprints encoding the relative positions of atoms in a molecule and representing molecular shape, namely the RDKit atom-pair fingerprint AP, our recently reported macromolecule extended atom-pair fingerprint MXFP, and the MinHashed Atom-pair fingerprint up to a diameter of four bonds MAP4. Finally, we also included two composition fingerprints, namely MACCS keys and molecular quantum numbers (MQN), which encode the presence and number of features present in a molecule.