To overcome the issues with factorial DoE algorithms, recent work has turned to machine learning (ML) strategies that use previous experimental data to suggest single experiments or small batches. By learning from each experiment, these ML strategies claim to have greater efficiency than factorial methods, which plan all experiments in a batch. Additionally, these strategies can test values throughout a range for each continuous factor, enabling more precise optimisation. ML has been applied to a wide variety of reactions including oxidation, Diels-Alder, methylation, Paal-Knorr, crosscoupling, asymmetric hydrogenation, and C-H activation. Choosing the most efficient optimisation strategy for a particular chemical reaction remains a challenge. The number of machine learning methods that could be used for reaction optimisation has exploded in recent years. Often strategies have been applied on different case studies of varying diffi-culty, and previous studies have shown that an algorithm which performs well on one reaction might be inefficient on another. Furthermore, each strategy has limited capabilities and can only be applied to certain types of optimisation problems. For example, the SNOBFIT optimisation algorithm has been used by several authors for optimising yield by changing continuous variables (e.g., temperature and stoichiometry), but it does not work with categorical variables such as solvents or bases. When ML strategies have been applied to selection of categorical variables, only a small number of categorical options have been used. However, recent work has shown that descriptors of a solvent or catalyst can be be used to extrapolate performance from a small number of experiments to a large library. An additional challenge is that there are often trade-offs between optimisation objectives. For example, using harsher conditions (i.e., higher temperature or concentration) will increase conversion in many reactions but also cause side reactions that reduce selectivity. In a broader sense, using a particular catalyst might increase reaction yield but be too expensive to use at commercial scale. These are called multi-objective optimisation problems, and their solutions are Pareto optimal points. Pareto optimal points are defined as a set of solutions where an improvement in one objective necessarily causes a worsening of the other. Recently, several approaches have been developed for multi-objective optimisation, but a rigorous study has not been completed to compare them all on standardised benchmarks.