For computational chemistry, active learning has been applied for various applications including drug design . For HTE, recent studies have applied active learning to select data points for neural network models. These had positive impact on reducing the number of experiments needed to generate a training set for predicting the reaction yield . However, active learning still struggles to show a significant performance gain compared to randomly selecting data points to query, so called random sampling, when only a few data points have been labelled . It is also possible that active learning performs differently in different settings, such as the initial pool of labelled data points and machine learning algorithm.