Setup. To evaluate our model's ability to generate novel, unique, and valid molecular graphs relative to our baseline models we further train the model with the best performance across both distribution and chemical metrics (α = 0.27, ρ = 0.42) for 50 epochs. 10,000 molecules were then generated and evaluated on the distribution metrics described above and compared to previously published results for the baseline models. Results. We report our results in Table . We observe that training our model for an additional 30 epochs improves validity scores significantly (over 10 percentage points), but not novelty scores (which show an improvement of only 2 percentage points). We note that flow models are particularly adept at generating unique molecules as they consistently score higher than unmasked VAEs and GANs (n.b. OR-GAN optimizes for unique molecules through its own reward network.)