The chemical space of the human exposome is ever expanding with a wider diversity of chemicals from both fate and toxicity points of view. The latest estimates of the environmentally relevant chemicals based on the chemical registries and production volumes are estimated to be between 350k and 800k. For most of these chemicals there is little to no knowledge about their environmental fate nor toxicity. Since the experimental assessment of the fate and toxicity of such a large number of chemicals is not feasible, modeling approaches to predict hazard indicators play an increasingly important role in chemical prioritization and risk assessment. Prediction of the physicochemical properties and the biological activity (e.g. aquatic toxicity) has been one of the main approaches to deal with the structural diversity in the chemical space. Most existing modeling strategies employ quantitative structure activity relationship (QSAR) models and rely on building linear and/or non-linear relationships between the structural descriptors and the modeled activity/property. These models are often built on very homogeneous training sets (i.e. similar chemical classes), hence the linearity assumption. In fact, recent efforts have been put into using more heterogeneous training sets as well as moving away from the linearity assumption. Independent from the level of heterogeneity of the training dataset, QSAR models are very limited in the number of measured activities as well as the number of chemicals evaluated (e.g. around 1000 chemicals). The main consequence of this limitation is the fact that the models are used in extrapolation mode when used for prediction. This implies that the new data points are not represented adequately by the chemicals within the training set, thus outside of the model applicability domain. The use of these models for extrapolation may potentially result in very large prediction errors. For these predicted and measured activities (i.e. toxicity and/or other properties) to be translated into chemical management actions, they are divided into different categories using thresholds based on expert knowledge. Examples for such categories are environmental hazard categories defined by the Globally Harmonized System of Classification and Labelling of Chemicals (GHS) or thresholds for persistence (P), bioaccumulation potential (B) and toxicity (T) defined under the European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH). The chemicals that fall within specific categories are then furthered for more active monitoring and eventually for legislation. This process triggers wider experimental evaluation of chemicals within high priority categories, which may result in adjustment of the previously set thresholds, based on the new experimental evidence. However, for this chemical management strategy to be effective, a more accurate and reliable chemical prioritization (i.e. chemical categorization) approach is warranted.