Iteratively acquiring these reactions and updating the model accordingly, we demonstrate that its generalizability across the defined chemical space improves rapidly, i.e., the discrepancy between predicted and computed variations in activation energy values of the acquired points gradually declines, and convergence is reached after only a handful iterations, cor-responding to the acquisition of fewer than 100 new data points. With the final, validated model at our disposal, new benchmarking datasets with particularly challenging reactions can be curated, and an informed estimate of the maximal errors across chemical space can be inferred.