20 Similar to experimental data, computational data sets also lead to the development of ML innovation . One example is the development of a new reaction representation based on the geometry of reactants and products . Unlike expertchosen descriptors, this representation is generalisable to other systems. Although not concerned with selectivity, Corminboeuf and coworkers recently reported OSCAR, a computational repository of 4,000 organocatalyst structures mined from the literature and Cambridge Structural Database (CSD) . To summarise, the last decade has seen a steady refinement in the representation of chemical species, considering sterics, electronic properties and non-covalent interactions. Since these interactions are governing any reactivity, accurate description is relevant for a successful ML campaign. Most of the work in organocatalysis using expert-derived descriptors has been conducted in the low to middle data-regime. Only recently, the focus has shifted towards bigger data sets of more than 1,000 reactions, the first one of which has already inspired a manifold of other groups to develop new ML techniques, including graph neural networks. With the continued rise of highthroughput experimentation in organocatalysis , we expect ML to be applied to more data sets in this domain to aid in answering a wider variety of research questions.