In our optimisation process, often involving reactions with sparse historical data, we prioritised thoroughly exploring a large set of categorical variables. From chemical experience, categorical variables such as ligands, solvents, and bases can substantially influence reaction outcomes, potentially creating distinct and isolated optima in the reaction yield landscape. Algorithmic exploration of these categorical variables enables identification of promising combinations, which can guide fine-tuning of continuous parameters such as catalyst loading and reaction time in later stages of the optimisation process. However, incorporating numerous categorical parameters increases the dimensionality and complexity of the search space, as molecular entities must be converted into numerical descriptors, unlike directly representable continuous variables. We represented the reaction condition space as a discrete combinatorial set of potential conditions composed of reaction parameters such as reagents, solvents, and temperatures deemed plausible by a chemist for a given reaction (Fig. ). This allowed for automatic filtering of impractical conditions such as those with reaction temperatures exceeding solvent boiling points or unsafe combinations like NaH and DMSO.