The success of closed-loop optimization algorithms strongly depends on the information included in the initialization data on which the initial model is trained. To assess the effect of the data used for initialization, we conducted a case study where the optimization is initialized using: (i) a broader set of reaction conditions from multiple ligands, and (ii) a restricted dataset that contains only reaction information from three ligands. Generally, ML models deliver better prediction for areas in the chemical space that are close to or within the training data. In Figure , two different extreme situations were compared -initializing the active learning either with local data (the dataset contains datapoints of only three ligands) or with random data (on average the dataset contains information of 7 ligands). By restricting the dataset we intentionally introduce biases (by showing the model only a very restricted part of the chemical space) in order to assess the impact on the closed-loop optimization. It is apparent that even though the local initialization possesses restricted knowledge, within ten iterations the model performance is approximately equal to an initialization dataset which is more diverse. These findings may be very beneficial for experimental chemists it proves that restricted initialization data can rapidly catch up with diverse initialization data when predicting experimental yields.