Machine learning applied to the chemical sciences has proved itself an important new tool for chemists and in particular computational chemists. The reported test error in chemical regression tasks is often similar or lower than for more computational demanding tasks such as DFT, making it attractive for especially high-throughput screening studies. For data driven methods such as machine learning there is a strong dependency on the training data distribution. With the vast and diverse nature of chemical space, a model with the same low error across chemical space is currently not realistic. Therefore, attention within the chemical machine learning community has lately turned towards quantifying the uncertainty on property predictions made by machine learning methods .