Here we must unbury the lead. MoFlowGAN achieves the highest compound distribution performance among all baseline models with 66.5% of generated molecules being novel, unique, and chemically valid. In fact, after 50 epochs of training MoFlow-GAN outperforms native parent model, MoFlow. Admittedly, MoFlow achieves near perfect performance (93.5% NUV) after 200 epochs of training on QM9 . We note that the major shortfall of our model is the low novelty score; we posit that weighting these distribution metrics more heavily in the early stages of training or using a RL component with stronger gradient signal will allow us to be competitive with the current state-of-the-art generative models on this front.