Active ML represents a strategy for continuous model optimization through the iterative improvement of the surrogate model by repeatedly retraining on new experimental data as it is collected. An objective, such as yield, can be rapidly maximized through the efficient exploration of chemical space, with experimental prioritization being guided by the surrogate model. Based on our preliminary modelling using different data representations/surrogate models, we aimed to assess how well these surrogate models perform within a closed-loop optimization framework. To allow for a fair comparison, the initial dataset was shuffled, a random batch was used for model initialization and the remaining data was stored as "Lookup-table" (Figure ). The initial batch size was varied -if not stated otherwise the models were initialized with 15 datapoints (7.5% of the dataset). Once the models were trained on the initialization data, yield predictions were generated using the relevant reaction feature-sets. The datapoint (or a batch of datapoints) with the highest yield predictions were selected for "experimental" evaluation and the true yield was transferred from the lookup table (serving as a simulated experiment) to the training dataset.