This work introduced an approach to explaining molecular property predictions based on molecules' graphical depictions, which we named contextual explainability. Our method is able to capture both basic (like atoms and bonds) as well as more complex structures (like rings and chemical groups), yielding explanations that are more aligned with chemists' intuition. We provided evidence that our contextual explanations possess several desirable properties: the attributions tend to be sparse, are robust with respect to the chosen graphical representation, and respect the symmetry of the input image. It would be interesting to explore our explanation framework in the context of property optimization: explanations in pixel space have the advantage that the model can explain a prediction not exclusively in terms of what is present in the given molecule, but also in terms of what is missing. The explanations could then be employed to provide suggestions of structure modifications for optimizing the given molecular property.