From advances in computer science and statistics , machine learning (ML) techniques, particularly Bayesian optimisation, have gained popularity in chemistry for their ability to successfully guide experimental design . Bayesian optimisation uses uncertainty-guided ML to balance exploration and exploitation of reaction spaces, identifying optimal reaction conditions in only a small subset of experiments. Bayesian optimisation has shown promising results in reaction optimisation, validated experimentally by multiple case studies and outperforming human experts in simulations . However, existing applications in reaction optimisation have been largely limited to small numbers of experiments in parallel batches of up to sixteen . Moreover, these approaches are often non-automated or are restricted to single reaction objectives. The constraints of small batch sizes necessitate more optimisation iterations to identify high-performing reaction configurations, particularly when exploring large reaction spaces. These limitations also impede integration with large-scale automation. Consequently, the full potential of highly parallel, automated reaction optimisation remains underexplored. In the pharmaceutical industry, where many reactions prove unsuccessful and timelines for drug discovery and development are continuously accelerating, there is a need to expedite optimisation strategies in chemical synthesis . The natural synergy between ML optimisation and HTE platforms, leveraging efficient data-driven search strategies with highly parallel screening of numerous reactions, offers promising prospects for automated and accelerated chemical process optimisation.