We sought to select a set of compounds from the public domain that approximate a typical corporate library including H, C, N, O, F, P, Cl, and S atoms. To this end, we sampled 5000 compounds from ChEMBL and compared them to the Johnson & Johnson Innovative Medicines corporate database. Compounds with molecular weights over 1200, more than 30 rotatable bonds, a quantitative estimate of drug-likeness (QED) score 57 under 0.4, and heavy atom count over 200 were removed. Then, we got a reduced ChEMBL set of compounds with similar molecular weights, numbers of rotatable bonds, and fraction of sp3 to the corporate database (see Fig. of the Supplementary Information (SI)). This subset was subjected to diversity selection, followed by manual inspection to remove molecules containing undesirable or unusual chemical substructures. As a result, we initially selected SMILES of molecular building blocks and typical lead-like compounds as well as a few protein degraders and macrocycles to produce a total of 2, 635 unique molecules with up to 60 non-hydrogen atoms (N ≤ 116). To have a more extensive sampling of the chemical space described by the selected SMILES, we generated all possible stereoisomers for each structure using the RDKit code-an open source toolkit developed for cheminformatics . In our script, we generate unique stereoisomers with the option to perturb each stereocenter while keeping the same atomic connectivity (i.e., tautomers are not considered), yielding a larger number of isomers whose stability is later checked via quantum mechanical (QM) calculations.