While these results are encouraging visual inspection of some of the best scoring molecules (first column of molecules in Figure ) show that they do not resemble drug-like molecules, indicating that they may be unstable and/or synthetically inaccessible. To quantify this property we compute a synthetic accessibility score using the Molecule.one retrosynthesis package which is based on machine learning algorithms and trained on a large number of reactions. Molecule.one returns a synthetic accessibility score between 1 (very synthetically accessible) 10 (not synthetically accessible) and the fraction of the 100 best-scoring molecules with a Molecule.one score below 10 is shown in Column 2 of Table . The only case for which a non-negligible fraction of molecules may be synthetically viable is DDR1 with 26% while for the rest that fraction is very close to 0%. This problem has been observed before for generative models by, for example, Gao and Coley , Brown et al. , and Renz et al. . We address this issue in three ways: we use Walters rd filters code (following Brown et al. ), a score modifier based on a heuristic synthetic accessibility (SA) score suggested by Gao and Coley , and a combination of the two. The results are shown in Table and show that the use of filters has relatively little effect on the number of molecules with better scores than the known binders and their average docking score . Unfortunately, there is also a negligible effect on the fraction of molecules deemed synthetically accessible molecules by Molecules.one (Table ). Inspection of the highest scoring molecules for each target (second column of molecules in Figure ) reveals that while the filters successfully prevented unstable bonding patterns, they do not prevent other reactive moieties such as cyclopentadienes and cyclohexadiene-like motifs.