Comparing these strategies by 5-fold cross validation, we found their performance to be similar (Table ). On average the highest uncertainty sampling converged fastest, but with the smaller dataset also had on average higher errors than random sampling. The training curves (Fig. ) make the differences between strategies clearer. Here we found the highest uncertainty sampling had faster convergence with significantly smaller variance as measured by both MAE and RMSE. By contrast, random sampling RMSE converged slower and with higher variance even at large dataset sizes. Interestingly, the highest uncertainty FPS strategy did not show improvement over regular highest uncertainty sampling. This may indicate that the weighting of FPS and highest uncertainty sampling needs adjusting. Nevertheless, the results suggests that there is no significant benefit of the strategy over regular highest uncertainty sampling, which from the oxalic acid results is the best of the three strategies for training NNPs from CSP datasets.