Next to this intuitive approach, learned machine learning representations allow to create a continuous representation which ensures the validity of the proposed queries . By training recently developed methodologies such as variational auto-encoders or generative adversarial neural networks on a set of synthesizable molecules or catalysts, a learned machine learning representation, a so-called latent space, can be developed which guarantees the synthesizability of the proposed queries . Upon this representation additional constraints on the catalyst or molecule can be enforced according to the application .