To assess optimisation algorithm performance, practitioners often conduct retrospective in silico optimisation campaigns over existing experimental datasets . This enables comparison of optimisation performance against previously established experimental optima within a set evaluation budget. However, publicly available experimental datasets usually contain only ∼ 1000 reaction conditions per substrate pair, especially those with multiple reaction objectives. This limited scope is insufficient to benchmark HTE optimisation campaigns involving multiple 24/48/96-well plates. To address this limitation, we conducted benchmarks against emulated virtual datasets, following similar established practices (Fig. ). , derived from experimental data, used for benchmarking in this study. b, The C-H arylation virtual dataset is generated by training an ML model on 1728 experimentally collected reactions from Torres et al. (EDBO+), then predicting reaction outcomes for a larger range of reaction conditions and variables not present in the original training data. This creates a large-scale virtual dataset suitable for benchmarking HTE optimisation campaigns (see Supplementary Information section 1). c, Distribution of reaction objectives, yield (%) and catalyst turnover number, for the first Suzuki Coupling (i) virtual dataset from Olympus . Pareto points represent the optimal multi-objective combinations. d, Distribution of reaction objectives, yield (%) and reaction cost, for the C-H arylation virtual dataset generated in this study. e, The hypervolume is used to assess the performance of optimisation algorithms. The hypervolume quantifies the volume enclosed by the optimal objective (yield-selectivity) combinations (Pareto points) identified by each algorithm. The hypervolume is used to compare algorithm performance against the best reaction conditions in the benchmark dataset, evaluating how effectively the best existing solutions are identified.