a way of exemplifying how the implicitly-learned scoring function may be applied in a realistic setting, in this section we use it to bias a generative model towards favorable regions of chemical space. We make use of the GuacaMol baselines 50 package and implemented a submodule with the proposed scoring function trained on all available rating data. We then chose the pretrained SMILES-based LSTM generative model and the hill-climbing optimization strategy to generate 500 molecules both maximizing and minimizing the learned scoring function. Some generated molecule examples are presented in Figure . Visually inspecting some of the examples maximized by the scoring function, we can appreciate that the model is assigning high scores (i.e., "unattractive") to compounds that feature long flexible chains, atypical groups such as phosphates or azides, conjugated double bonds and reactive pieces, and overall higher number of carboxylates and alcohols, among many other non-drug like properties. On the other hand, minimizing the learned scoring function results in a reasonable mix of aromatic rings and aliphatic sp 3 carbons, reasonably-sized fragments as well as several typical groups featured in drug-like molecules. From these qualitative analyses we conclude that the scoring function has successfully captured a reasonable degree of chemical intuition.