Advancements in automation have catalysed a shift in chemical reaction optimisation , particularly through the emergence of chemical high-throughput experimentation (HTE) adapted from techniques in biology . HTE platforms, utilising miniaturised reaction scales and automated robotic tools, enable parallel execution of numerous reactions. This allows for the exploration of many combinations of reaction conditions, making HTE more cost-and time-efficient than traditional techniques relying solely on chemical intuition and one-factor-at-a-time (OFAT) approaches . However, as additional reaction parameters multiplicatively expand the space of possible experimental configurations, exhaustive screening approaches remain intractable for larger design spaces, even with HTE . Consequently, HTE chemists rely on chemical intuition to navigate vast reaction spaces effectively. A common approach is designing fractional factorial screening plates with grid-like structures (Fig. ), as used in our process chemistry HTE lab . While these structures effectively distill chemical intuition into plate design, they explore only a limited subset of fixed combinations. This limitation, especially in broad reaction condition spaces, may lead experimentalists to overlook important regions of the chemical landscape. The initial experiments are selected with quasi-random Sobol sampling , diversely sampling from the reaction condition space. Then, iterative Bayesian optimisation suggests subsequent HTE screening plates, optimising towards defined objectives. This process is typically repeated until convergence, stagnation in improvement, or exhaustion of experimental budget. Then, a fully exploitative ML approach leverages accumulated data from all HTE plates in the campaign to maximise final reaction objectives. Finally, feature analysis elucidates reactivity trends in the optimisation campaign.