In real-world scenarios, chemists often face the challenge of optimising multiple reaction objectives simultaneously, such as maximising yield while minimising cost. HTE campaigns, characterised by larger batch sizes and range of reaction parameters, further amplify optimisation complexity. Com-putationally, scaling the parallel optimisation of multiple competing objectives towards high batch sizes is challenging and incurs considerable computational load . For example, the q-Expected Hypervolume Improvement (q-EHVI) acquisition function , a popular multi-objective acquisition function applied previously in reaction optimisation , has time and memory space complexity scaling exponentially with batch size . Given the limited scalability of such approaches (see Supplementary Information section 2), we sought to develop a more scalable optimisation framework for highly parallel HTE applications, incorporating several scalable multi-objective acquisition functions in our work: q-NParEgo , Thompson sampling with hypervolume improvement (TS-HVI) , and q-Noisy Expected Hypervolume Improvement (q-NEHVI) (see Methods for further details).