As we have shown above, one can learn a great deal about the ML model from heatmaps by comparing to the corresponding ground truth heatmaps. However, ground truth heatmaps will not be available for other properties so tools for assessing the quality of the heatmap in the absence of the ground truth would be very useful. We investigate two approaches: uncertainty in the atomic attribution (UAA) and atomic attribution of the uncertainty (AAU). The UAA is obtained by computing the atomic attributions for each tree in the random forest and computing a standard deviation for each atom while the AAU is computed as described above for logP except using the standard deviation of the logP predictions instead of the logP value itself. A more detailed description as well as examples for the four molecules described above can be found in Section S1. We find that while these uncertainty-guided heatmaps can to some extent be used to indicate problematic regions of the molecules, the predictive power of either of the approaches is not strong enough to make them especially useful for this model/XAI combination.