Extended connectivity fingerprints (ECFPs) are commonly used descriptors in ligand-based virtual screening. However, ECFPs encode only structural information. More abstract encodings pertaining to the prediction of physicochemical properties can be better expressed using latent space representations generated from deep learning models (i.e., replacing use of fingerprints with latent space representations to generate a projection). In Figure , we illustrate the representation of the fragments for both the latent space from a deep learning model (left) and the ECFP4 fingerprint (right). We highlight and color only the fragments known to bind to the FP subpocket. Regarding the positioning of the fragments, the visualizations suggest that the latent space generates a smoother representation compared to the ECFP4 fingerprint space. This makes intuitive sense since ECFP4 is a 2048-dimensional bitwise fingerprint based fully on structural features, whereas the deep learning representation is a 256-dimensional continuous vector. In the left part of Figure , we colored the fragments by the predicted solubility and see that most of them are predicted to be soluble (i.e., they are between yellow and green). The fact that the analyzed FP fragments have generally higher predicted solubility is congruent with chemical rationalizations given in . Since the ECFP4 fingerprint is not by itself predictive, we only highlight whether the compound is found in the FP subpocket or not in Figure (right).