In terms of reaction variables, we selected a choice of electrophiles, equivalents of electrophile and NaOH, a choice of solvents, and flow rate. Equivalents and flow rate were used as continuous variables, and electrophile and solvent choices were categorical variables. The ranges of the reaction variables were selected based on previous experience in developing flow process using highly active species. 24 To start reaction optimisation, initial experimental results are required which are usually collected by a design of experiments method. In this case, we used MaxPro (Maximum Projection design in R package 25 ) which has been shown to have the good space-filling properties in projections to all subsets of factors. Based on the initial results, individual probabilistic surrogate models can be trained for each objective. In this case, we used Gaussian processes, which perform well in the low data limit. For the categorical variables (i.e. solvent and electrophile choices), one-hot encoding was used and incorporated in the optimisation of the acquisition function using the rounding trick by Garrido-Merchán. To test performance of the new algorithm, we first benchmarked between qNEHVI and TSEMO 10 in silico using the Python package Summit. Both algorithms started with 20 initial experiments and optimised for another 20 experiments. Each algorithm was executed repeatedly 20 times, and the 96% confidence intervals were reported. Performance of two algorithms was compared by calculating hypervolumes after each iteration.