It was observed that OHE clearly outperformed the other representations after 10 iterations were achieved and reached the optimal set of conditions in the fewest number of iterations (Figure ). In a similar manner, recent results by Shields et al. observed that OHE delivers approximately equal performance compared to hand-crafted DFT descriptors during Bayesian optimization of organic reaction conditions. Initially, we assumed that the superiority of OHE performance could be due to the full factorial chemical space since all possible parameter combinations could be evaluated. Whilst this effect would benefit all representations, we hypothesized that the simplicity of OHE along with a full factorial space could be more beneficial when compared to the effect on other input features (e.g. fingerprints) that are far more complex and might represent a challenge for the model to detect patterns in the data. To test this assumption, we dropped a random selection of the datapoints of the entire dataset (25%), therefore no longer representing a full factorial chemical space. However, we still observed that OHE outperformed the full feature set (see SI). Another reason for the good performance of OHE might be that during each optimization experiment the model receive datapoints of the same ligand multiple times (in combination with a different pre-catalyst or boronate). As discussed previously, the impact of the ligand is significantly higher to reaction outcome, compared to the other two parameters. As a result, it is likely that OHE captures the variability between ligands and therefore can efficiently identify high yielding reaction conditions. All previous experiments were conducted in a sequential design -during each iteration of the active ML algorithm, one decision was made and one single datapoint was sampled from the lookup table. In a real world setting this means that after each single experiment the yield is evaluated and then added to the ML training data. However, typically organic chemists conduct multiple experiments in parallel to accelerate the process of finding optimal conditions. Therefore, batch-sequential sampling within active learning represents a more realistic approach and was also investigated. The ideal batch size is a trade-off: a large batch size typically brings benefits to experimental workflows as HTE equipment can be applied, for example screening 96 conditions at a time. If the experimenter gains more useful data per HTE run, this will often lead to minimizing the total number of HTE runs and be less time intensive. However, large batch sizes at the beginning of the active learning strategy could lead to the acquisition of chemically redundant data as the active learning model may make low informative predictions due to the initially limited number of datapoints used for training.