Initial training data for the GP in all cases was generated using Sobol sampling with PyTorch , employing low-discrepancy Sobol sequences to obtain quasi-random points uniformly covering the multi-dimensional reaction space, providing superior distribution and space filling properties than standard random sampling . Similar methods like centroidal Voronoi tessellation (CVT) and Latin hypercube sampling (LHS), have shown superior optimisation performance compared to random initialisation . Training inputs and targets for all GPs are normalised and standardised, respectively, according to the specifications in BoTorch . As we aimed to focus the benchmarks on comparing differences between acquisition functions, we used a GP with general purpose kernel hyperparameters adapted from EDBO+ for all benchmarks. All acquisition functions were implemented using BoTorch . We implemented KeOps and fast variance estimates from GPyTorch to enable memory-efficient computations. We used PyTorch Lightning to set random seeds, running 20 repeats from seed 1 to 20 for each benchmark result. All computations were run on a workstation with an AMD Ryzen 9 5900X 12-Core CPU and a RTX 3090 (24GB) GPU.