As a baseline comparison, we first attempted to optimise this Suzuki reaction using purely expertdesigned HTE screening plates with the grid template structure shown in Figure . Two expertdesigned 96-well HTE plates were conducted (Fig. ), each containing 48 unique reactions run in duplicate. These plates yielded only trace amounts of product, with the best reaction achieving ∼ 5 (area percent) AP yield based on Liquid Chromatography-Mass Spectroscopy (LC-MS) analysis (see Methods). Often, the absence of initial reaction hits could lead to premature conclusions regarding the reaction's viability. Subsequently, without incorporating the previous data, we re-attempted this optimisation from scratch, applying our ML optimisation workflow to this Suzuki reaction within the defined search space of 88, 000 reaction conditions. We initiated the first iteration of this workflow using Sobol sampling to select the initial 96-well HTE experiments to maximise reaction space coverage, followed by model-guided Bayesian optimisation in subsequent iterations. The first Sobol sampling iteration surpassed the results of the two expert-designed plates, identifying multiple reaction hits with up to 51 AP yield and 86 AP selectivity (Fig. ). The second and third iterations of Bayesian optimisation further improved on both AP yield and AP selectivity. By the fourth iteration, we observed no improvement in maximum AP yield and selectivity over the prior three iterations, prompting us to conduct one final iteration before terminating the campaign. We noticed that the experiments selected by the Bayesian optimiser for the fifth iteration were still largely explorative, with many experiments suggesting the use of PhMe solvent, which had been minimally explored until this point. Therefore, we decided to implement a fully exploitative optimisation strategy in the final iteration of the campaign, aiming to maximally exploit all information accumulated in the campaign thus far, at the expense of any further exploration. We neglected model uncertainty and selected promising experiments with the highest predicted AP yield and AP selectivity values using Utopia point scalarisation (see Methods). This exploitative approach in the final iteration identified multiple reactions with improved AP yield (up to 76%) with high AP selectivity (up to 92%) compared to previous iterations. These high-performing reactions, conducted at relatively lower temperatures beneficial for process scale-up, represent promising hits that would typically progress to fine-tuning of quantitative factors like catalyst loading and reagent equivalents in industrial process development. For comparison, we also performed the originally suggested HTE plate for the final iteration, confirming the superior performance of the pure exploitative approach, highlighting the merits of leveraging accumulated information in the final stage of an optimisation campaign (see Supplementary Information section 5). From experience, we expected initially that the choice of ligand would be the dominant factor influencing product formation. However, we observed throughout the campaign that this Suzuki reaction was highly sensitive to the choice of nickel precatalyst, with the [Ni(oTol)(Cl)(PPh 3 ) 2 ] precatalyst present in most of the high-performing reaction conditions. In contrast, different ligands seemed to have comparatively minimal impact on reaction outcomes. A Shapley value analysis of the collected data using SHAP (Shapley additive explanations) corroborated these observations (see Methods), identifying the presence or absence of the [Ni(oTol)(Cl)(PPh 3 ) 2 ] precatalyst as the most important feature for AP yield, represented by the binary value of the one-hot encoded feature (Fig. ). Consistent with our observations, features representing bases, solvents, co-solvents, and temperature were also attributed higher importance than those of the ligands. In fact, when we assessed the most promising conditions on 500 mg scale, we observed essentially identical results with and without added ligand, indicating that [Ni(oTol)(Cl)(PPh 3 ) 2 ] catalyses the reaction. This departure from expected chemical reactivity explains the minimal product formed in the two expertdesigned HTE plates, which used the less active [NiCl(oTol)(TMEDA)] and [Ni(COD)(DQ)] nickel precatalysts. This underscores the limitations of traditional grid-like HTE plate designs, which are restricted in the diversity of reaction parameters explored in each plate and risk overlooking promising regions. In large multidimensional reaction spaces with multiple categorical parameters, as in this case study, this limitation is further pronounced. In such complex landscapes, the presence of reactivity cliffs renders the reaction space challenging for experimentalists to navigate and ensure comprehensive exploration . Consequently, this could lead to erroneous premature conclusions about reaction viability. Our optimisation workflow provides chemists with a valuable tool to efficiently navigate these challenging and intricate spaces, potentially uncovering unexpected reactivity patterns missed by traditional approaches.