Together, these results demonstrate that SMILES exploration is suitable for the generation of novel compounds that are similar to a starting compound, although it is less efficient for generating compounds with desirable bioactivities compared to reinforcement learning. 81 SNN is the similarity to the nearest neighbor of 10 000 molecules from the generated sets compared to the set of 1627 known AURKA and AURKB ligands. KL divergence describes the similarity of the physicochemical property distributions of 10 000 molecules from the generated sets compared to the know ligands.