So far, the main contribution of computational chemistry methods to catalyst discovery has been in establishing a mechanism for catalytic cycles of known catalysts. Current state of the art within computationally driven catalyst discovery is generally focused on improving known catalysts based on the catalytic mechanism. One approach is to screen large libraries of molecules that are structurally similar to the known catalyst. For example Nandy et al. combined machine learning (ML) and density functional theory (DFT) methods to screen a library of 16 million candidates for the catalysis of the methane to methanol oxidation. Another recent example of computationally driven catalyst optimization from Cramer et al. used mechanistic information of three intertwined catalytic cycles for CO 2 conversion to formic acid, formaldehyde and methanol to generate theoretically founded rules for predicting catalytic activity and selectivity. The theoretical framework was used to predict a catalyst that optimizes selectivity towards formaldehyde, which was verified experimentally with an 81% yield. Das et al. mapped catalytic activity in CO 2 hydrogenation to acid/base properties of frustrated Lewis pairs to predict a specific combination of a Lewis acid and a Lewis base from approx 4000 candidate pairs which was experimentally verified to catalyze the reaction. An alternative to the screening approach is to use generative models to propose new catalyst candidates. The advantage of this approach is that one is not limited to discovering catalyst candidates already present in a predefined library. One such approach; a graph-based genetic algorithm (GA), was recently employed by our group to discover a new catalyst with a previously untested structural motif for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. The proposed catalyst candidate was experimentally verified to be eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), a commonly used catalyst for the reaction. Vital to these important findings is mechanistic insight into the relevant catalytic cycle. Typically, these catalytic cycles are generated by a combination of experimental work, expert knowledge and quantum chemical calculations, making this step a laborious undertaking. Moreover, someone needs to actually have the idea of testing a specific molecular moiety as a catalyst for a reaction. Thus, a method for truly de novo catalyst discovery must also include a solution for this part of the discovery process.