One caveat that we had experimentally observed during molecular generation is that it was useful to constrain or stop optimization of the scoring function once it had reached values close to the limits to the empirical distribution of learned scores (|ŝ| ≈ 9 using the reported regularization strategy during training in our sets). Not doing so resulted in a certain degree of quirkiness and invalid molecules, which we attribute to the generative algorithm overexploiting the scoring function on regions of chemical space that it had not previously observed during training. Additional details on the generative model and optimization hyperparameters are made available in the accompanying code repository to this work.