For noiseless datasets, the reducible source of error is divided in a bias and variance term. Our recent application of Bayesian inference to ensembling allows users to quantify the error in both reducible contributions . By separating the contributions, it becomes possible to prioritize efforts between reducing model bias and model variance. Reducing the model bias error is tedious and requires user experience. Bias can be reduced by adding more data, and by choosing the best possible molecular representation, model architecture and set of features to relate the molecular structure to the target property. These challenges are particularly common in chemistry; the vast chemical space makes data size and coverage a large source of error compared to other fields of research, where many chemical structures are unique or under-represented in (experimental) datasets. The representation of molecules inside machine learning is without question one of the main challenges in chemical property prediction today. In other fields of machine learning such as computer vision or natural language processing, the size of an image or a sentence does usually not scale with the output target. For example, the number of words in a sentence or letters in a word do not tell us about its meaning, conveyed information or sentience. In contrast, for extensive properties the size of a molecule changes its properties significantly, so that representations and architectures developed in other fields of research must be properly adapted to chemical applications. Careful consideration of several representations and selecting the most appropriate for the target property is crucial in reducing the bias error. Properties may not always be easily delineated between intensive and extensive, so we recommend choosing extensive aggregations in chemical systems when in doubt.