Colorectal cancer patients would benefit from a valid, reliable and efficient detection of Tumor Budding (TB), as this is a proven prognostic biomarker. We explored the application of deep learning techniques to detect TB in Hematoxylin and Eosin (H&E) stained slides, and used convolutional neural networks to classify image patches as containing tumor buds, tumor glands and background. As a reference standard for training we stained slides both with H&E and immunohistochemistry (IHC), where one pathologist first annotated buds in IHC and then transferred the obtained annotations to the corresponding H&E image. We show the effectiveness of the proposed three-class approach, which allows to substantially reduce the amount of false positives, especially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.