Herein we employ Bayesian optimisation to steer an experimental campaign towards the simultaneous improvement of all key metrics for a five-component self-assembled photocatalytic molecular CO2 reduction system. This system was selected because the interdependent variables that control supramolecular structure make it challenging to optimise and representative of complex photocatalytic systems. Defining an objective function allowed simultaneous improvement of performance metrics, namely catalyst turnover number, turnover frequency, quantum yield and moles of product, without loss of selectivity. Concurrently, the use of Bayesian optimisation reduced the number of experiments required to optimise the system from a theoretical 10 5 down to ~100 and made the work accessible for a human experimentalist with standard laboratory equipment. Acquiring a systematic dataset that maps the parameter space then enabled the application of machine learning algorithms to extract relationships between the dependent variables. Furthermore, the role and importance of different parameters on the overall performance was revealed using Shapley additive explanations (SHAP). Finally, the problem dimensionality was reduced and parameter regions likely to further enhance system performance are exposed by controlling feature grouping. Thereby, the system was holistically optimised and deeper insight into the ruling parameters for each figure of merit was extracted.