For purely exploitative algorithms, a selected model could have suggested conditions near the best result (e.g., 71% yield) provided in the training data (Table To benchmark the learning efficiency of NEMO, the algorithm performance was evaluated using different dataset sizes. During the sampling, NEMO was forced to sample one condition per iteration from the pool of experimentally validated datasets based on highest EHVI. In the first benchmark, NEMO was trained on 16 data points, two conditions per each of the eight solvents (Figure ). The training dataset included a point on the Pareto front (Table , row 9).