Aligning with the standard number of reaction vials in solid-dispensing HTE workflows, we benchmarked our optimisation approaches using batch sizes of 24, 48, and 96 for 5 iterations, using Sobol sampling for initial batch selection in the first iteration. We compared the three acquisition functions (q-NEHVI, q-NParEgo, and TS-HVI) against a Sobol baseline. All acquisition functions showed comparable performance, outperforming the Sobol baseline on all datasets and demonstrating scalability to large batch sizes of 96. As convergence to optimal solutions was generally achieved on most benchmark datasets within the lowest batch setting of batch size 24 (120 total experiments), we focused our investigation on these results (Fig. ). Although the best acquisition function varied across each dataset, q-NEHVI consistently performed well on all benchmarks (see Supplementary Information section 2 for statistical tests), and was thus selected for further analysis. We also compared our approach to other reaction optimisation software (see Supplementary Information section 2), demonstrating improved scalability to large batch sizes and search spaces. Our benchmark datasets included various reaction condition featurisation methods including Density Functional Theory (DFT), with high-dimensional reaction representations up to 530 dimensions. While we have not explicitly tested upper computational limits of our approaches, we demonstrated robust scalability across dataset sizes, feature dimensions, and batch sizes examined in this study. All benchmarks results can be found in the Supplementary Information. All displayed benchmark results used batch size of 24 for 5 iterations using the hypervolume (%) metric to assess reaction conditions identified by the optimisation algorithms compared to the best conditions in the ground truth dataset (see Supplementary Information section 2 for benchmarks on all datasets and batch sizes 48/96). All optimisations were repeated across 20 different random seeds, plotting the mean hypervolume (%) and ± 1 standard deviation. a, Comparison of different scalable multi-objective acquisition functions against a quasi-random Sobol baseline. b, Optimisation performance of the q-NEHVI acquisition function with varying magnitudes of Gaussian noise added to model training data. c, Optimisation performance of the q-NEHVI acquisition function with the nested and naive constrained strategies to accommodate constraints of 1 or 2 unique temperatures per batch.