We have explored active learning on two different high-throughput experimentation datasets. The aim was to investigate if active learning can be used to efficiently learn to predict reaction yields with a certain AUROC. We focused on comparing the uncertainty-based active learning strategy Margin to random sampling, and investigated matrix factorization, random forest, simple and complex neural networks. In our study we explored initial pools of 10, 100 and 1000 labelled data points.