Multi-objective Bayesian optimization (MOBO) is a powerful tool applied in multiple stages of chemical reaction development . This includes, for example, the discovery of multi-functional molecules , identifying the best reaction conditions to achieve high yield with good selectivity , and processes design where improving the yield while considering the throughput or environmental impacts at the same time . In the presence of multiple objectives that need to be optimized simultaneously, MOBO can find a set of solutions that represent a trade-off among the objectives, known as the Pareto front.