showing that initialization with minimal data led to ineffective optimization whilst initialization with out-of-sample data still allows the active ML model to rapidly find ideal conditions. Most importantly, when comparing initialization of the closed-loop optimization with data that included the full feature set (fingerprints, DFT descriptors) to a double-sized dataset that was encoded with OHE (no chemical information), the latter identified the highest yield conditions in fewer experiments. Moreover, we found that increasing complexity of the parameterization requires a larger initialization dataset to deliver comparable performance.