Enantioselective organocatalysis stands as a powerful strategy for the stereocontrolled synthesis of diverse molecules. While computational modelling has traditionally been employed to quantify and elucidate the origin of enantioselectivity, it remains challenging and costly due to the small energy differences between the two competing transition states determining selectivity. This challenge is exacerbated by the vast conformational flexibility of these systems, rendering transition state optimization difficult to converge and excessively resourceintensive. ML has emerged as a promising approach to circumvent the need for exhaustive transition state analyses. Over the last few years, several ML models have been published to assist in the prediction of reaction outcomes, reaction optimisation and catalyst design. 106