Data that is closely related, but not similar in nature, can also serve as initialization of active machine learning models . For example, when modeling reactions with one type of catalyst and literature data on another catalyst is available, this might still contain valuable information for an active learning model . With active transfer learning, the goal is to leverage this knowledge from nearly similar data to obtain a machine learning model with an improved perception of the examined problem. In this way, rudimentary physical knowledge is introduced in the machine learning model which again improves the initial experimental selection. This methodology has been proven to work on reaction yield classification of cross-coupling reactions, by pretraining a machine learning model on reactions with different nucleophiles .