We trained ML regressors on reaction datasets from Torres et al. (EDBO+), using ML predictions to emulate reaction outcomes for a broader range of conditions (e.g., temperature, concentration) than present in the original experimental training data (Fig. ). This emulation expands smaller experimental datasets to larger-scale virtual datasets more suitable for benchmarking HTE optimisation campaigns (Fig. and). Additionally, we incorporated similar virtual datasets in our benchmarking from Olympus (Fig. ) (see Supplementary Information section 1 for details on all benchmark datasets). To evaluate optimisation performance, we used the hypervolume metric to quantify the quality of reaction conditions identified by the algorithms. The hypervolume calculates the volume of objective space (e.g., yield, selectivity) enclosed by the set of reaction conditions selected by our algorithm (Fig. ). The hypervolume considers both the convergence towards optimal reaction objectives and diversity, providing a comprehensive measure of optimisation performance. We compared the hypervolume (%) of the reaction conditions obtained by each algorithm to that of the best conditions in the original benchmark dataset, using the latter as a reference for the true optimal solutions.