Sydow et al. provided a recombined ligand library of over 6 million potential kinase inhibitors, helpfully scoring the ligands based on their closest chemical similarity to compounds found in the ChEMBL database , as measured by the Tanimoto similarity. By using this information, we can quickly identify regions in a projection where the recombined compounds are similar to known molecules. We therefore projected the recombined ligands based on the latent space from a deep learning model, as was done for fragments in Figure left. We utilized only ligands with a Tanimoto similarity greater than 0.8 to at least one ligand in ChEMBL. Then, we colored the compounds according to their similarity to known ligands in ChEMBL (Figure ). This view of the recombined ligand space allows focusing on specific regions that are densely populated in compounds highly similar to existing compounds. The selected region is enlarged for a closer view, and several relevant chemical structures are revealed. We speculate that compounds that are different from the known ChEMBL molecules ("Distant ligands" in Figure ) but positioned closer to more ChEMBL-similar molecules in the fingerprint space are more likely to represent promising ligands than recombined molecules that are in dark blue regions (none of their neighbors is close to a known molecule).