While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always representative of another due to the variability in the acquisition and staining processes. Therefore, a classifier trained on the first may perform poorly on the second and additional annotations may be required. To remove this cumbersome requirement, we introduce an Unsupervised Domain Adaptation approach that can leverage annotated data from one brain area to train a classifier that applies to another for which no labeled data is available. To this end, we establish noisy visual correspondences between the two areas and develop a Multiple Instance Learning approach to exploiting them. We demonstrate the benefits of our approach over several baselines for the purpose of synapse and mitochondria segmentation in EM stacks of different parts of mouse brains.