Schilter et al. developed a variational-autoencoder trained on the dataset from Meyer et al. and were able to discover novel catalysts with favourable energy differences by optimizing in a learned latent space. They show that the distribution of coordination sites for the generated molecules follows the distribution of the training data. This indicates, that they find novel ligands by interpolating in the latent space, but do not discover novel binding motifs in a different area of chemical space than what their training data contains.