Common algorithms that use machine learning to learn to generate molecules (most commonly through learning the language of some string representation of a molecule, such as SMILES) are quick to generate, but also lack the specificity to the target in the generation phase and tend to have high similarity between generated molecules leading to many redundancies . Recent methods have also tried to replace the docking step with machine-learning-based screening . This, however, pushes the problem of being able to train an accurate model, which requires at least some known binders.