Since their introduction, a wide range of deep generative models have been proposed for molecular SMILES , graphs , and point clouds , based on a variety of architectures, from variational autoencoders (VAEs) and generative adversarial networks (GANs) to normalizing flows and denoising diffusion models . In this work we focus on GANs and flow-based models, introducing MoFlowGAN a normalizing flow that can be trained adversarially and is heuristically biased towards generating high performing drug-like molecules as a tool for molecular discovery. Like its name, MoFlowGAN is an amalgamation of ideas from MoFlow , Flow-GAN , and MolGAN ; each being quite successful in their own respects we aim to draw from their strengths to make up for their weaknesses.