We note that the quality of the predictions of SISSO models can be improved with more data acquired in a systematic way using active learning. By training ensembles of SISSO models with different subsets of training data or with different subsets of primary features, a statistical distribution of analytical expressions can be created. These expressions all describe the training data well. However, the predictions of the different expressions for new materials present a variance, which quantifies the uncertainty of the predictions. This uncertainty can be used to steer the acquisition of new data in unexplored regions of the materials space. Thus, larger portions of materials space can be systematically covered. Furthermore, the active-learning framework can also focus the acquisition of new data corresponding to the high-performance region of the materials-property map to improve the reliability of the SISSO description in this most important region.