In this case, adding data is an important factor to decrease model bias and arrive at a model that is mainly limited by errors stemming from variance. In general, 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 implications of this shortcoming on uncertainty estimations are discussed later in this work. In the following, we first investigate other possible sources of model bias.