sampling of a literature mined reaction space to cover a wide chemical space. Based on literature data, the authors constructed a model to predict the enantioselectivity of a reaction, while the turn-over frequency was estimated based on a volcano plot . Using fragments derived from their OSCAR database, the authors used the NaviCatGA genetic algorithm to find the most general catalysts. The fitness function comprised multiple objectives, including the median of the enantioselectivity and activity across the generality probing set. The usage of a multi-objective optimization algorithm allowed them to discover multiple trade-off optima, enabling a scientist to select the ideal catalyst based on the specific requirements of catalytic activity and selectivity, while still accounting for catalyst generality through design of the objectives. Noticeably, data analysis allowed to identify regions in the chemical space where highly ranked catalysts underperform as well as less sensitive areas in chemical space, further providing mechanistic insight into the mechanism of stereoinduction and activity trends.