There are a large number of optimisation strategies that could be compared in this work. However, we are most interested in strategies designed to achieve state of the art performance in a small number of iterations; this naturally excludes many evolutionary and gradient based optimisation techniques. We opt to implement in Summit seven different strategies that have been used for reaction optimisation previously but not compared in a standard fashion, as shown in Figure . Nelder-Mead simplex is a local optimisation strategy that builds simplexes, or multidimensional triangles, that are gradually shrunk to approach the optimum value. SNOBFIT builds quadratic response surfaces around the experimental data and optimises these to find new experimental conditions. Bayesian optimisation is a class of strategies that train a probabilistic model to predict the objectives given the experimental conditions; sample this probabilistic model; and optimise it in silico to predict the best subsequent conditions. We develop implementations of three promising Bayesian optimisation strategies: single objective Bayesian optimisation (SOBO), Gryffin and TSEMO. Finally, we develop an implementation of the Deep Reaction Optimiser (DRO) proposed by Zhou et al., which uses a pretrained reinforcement learning agent to optimise reactions. Further details of each strategy can be found in the SI. As a baseline, we also include a random strategy.