Opening black-box models to extract insights and learn or validate physical knowledge about the reaction has gathered attention in the concept of explainable AI, to bridge the gap between physical models and black-box models. We implemented two approaches -partial dependence plot (PDP) and permutation feature importance (PFI) -to analyse the sensitivity of the objectives (yield and cost) to continuous variables and solvent descriptors. In PFI, importance of a feature is calculated based on the model score when that feature is randomly shuffled. PDP, on the other hand, shows whether a relationship between a feature or two features with the target is linear, monotonic or more complex. Both approaches identified sig3 (asymmetry of s-profile) as the most important variable for yield (Figure ). Lower values of sig3 corresponded to higher yield, identifying dichloromethane (DCM) and 1,2dichloroethane (DCE) as the best solvents amongst 20 candidates included in the optimisation.