Our strategy is based on the fact that 1) deep layers in neural networks learn high-level concepts and 2) for pure convolutional networks, the value of each "superpixel" is determined by its receptive field in input space . We combine these two properties by tracing back attributions to pixel space through the Img2Mol encoder instead of the original CDDD encoder. We remark that this is possible since both encoders map the respective inputs to the CDDD space. Explicitly, let Λ : C → R be the QSAR downstream model and Img2Mol : M → C, where M [0, 255] 224×224 is the input space consisting of images of 224 × 224 pixels. We then construct the network