The peak at the similarity score of 1.0 is indicative of a mode collapse. Mode collapse is a problem seen with generative models where they learn a solution that technically does minimize the loss but miss "modes", resulting in the generation of a very narrow band of results from the complete space available to them . In our case, we only obtain molecules with aromatic rings strung together. This is not very useful, and we needed to regularize it. The word "regularize" is used here as a parallel to the regularization in polynomial fitting, where the model's quality is traded for a more useful model that can be extrapolated more reliably (bias-variance trade-off). In our case, regularization allows the model to sacrifice some synthesizability to generate a more diverse range of molecules. We observed that heuristically one requires the peak to stay below 0.55 for 300 molecules with no significant peak at 1.0 to get a diverse set of molecules.