Ultimately, we hypothesise that multi-fidelity data and architectures are the natural step forward for a wide variety of molecular tasks specified by small-sample datasets. Transfer learning by embeddings is particularly interesting due to its high effectiveness and wide applicability. For instance, we have shown that the embeddings can successfully be used by models such as random forests and support vector machines, and envision applications to probabilistic methods such as Gaussian processes that can provide uncertainty estimates. Furthermore, the supervised variational graph autoencoder architecture has the potential to be useful in a generative setting, allowing a more informed and varied compound generation protocol since primary screens are designed to be diverse and are between 4 and 8 times larger than the commonly used ZINC dataset . Another promising direction might be to train low-fidelity predictors on multiple HTS projects at the same time, including potentially hundreds of protein targets, and aiming towards a 'universal' latent space organised by function or protein-ligand interactions. Extensions to other drug discovery technologies such as DNA-encoded molecule libraries are also an exciting direction.