Then, based on these preliminary findings, we conducted simulated closed-loop optimization experiments wherein the impact of feature complexity on active learning performance was assessed. Unexpectedly, OHE outperformed complex parameterizations that incorporated chemical information even in low data regimes which are used to initialize active learning models. To understand the impact of initialization of the closed-loop optimization, different sized initialization datasets and differently sampled data (random, out-of-sample) were used,