One tool of growing importance in the arsenal of the biomolecular designer is generative deep learning, a subset of deep learning . Deep learning, in which a computer optimizes the parameterization of a neural network model in a data-rich regime, has become a powerful player in recent years by leveraging the increasing amounts of scientific data and the phenomenal growth in computational power over the last decades. Neural networks are formed by connecting simple functional units known as "perceptrons" through their inputs and outputs to form complex function approximators. Non-linear activation functions between the linear operations increase model representational capacity at the cost of having to solve a non-convex optimisation task . Generative deep learning in specific refers to the use of a model trained from a large body of data to sample new datapoints from an underlying probability distribution intended to mimic as closely as possible that of the training data. One particular variety of generative deep learning model demonstrating competitive performance in the sphere of biomolecular design is the variational auto-encoder (VAE) and its variants , in which data points are explicitly embedded in a smooth, continuous "latent space" that can function as a search space for optimization .