Recently, content-based image retrieval has been investigated for histopathological image analysis, focusing on improving the accuracy and scalability. The main motivation is to interpret a new image (i.e., query image) by searching among a potentially large-scale database of training images in real-time. Hashing methods have been employed because of their promising performance. However, most previous works apply hashing algorithms on the whole images, while the important information of histopathological images usually lies in individual cells. In addition, they usually only hash one type of features, even though it is often necessary to inspect multiple cues of cells. Therefore, we propose a probabilistic-based hashing framework to model multiple cues of cells for accurate analysis of histopathological images. Specifically, each cue of a cell is compressed as binary codes by kernelized and supervised hashing, and the importance of each hash entry is determined adaptively according to its discriminativity, which can be represented as probability scores. Given these scores, we also propose several feature fusion and selection schemes to integrate their strengths. The classification of the whole image is conducted by aggregating the results from multiple cues of all cells. We apply our algorithm on differentiating adenocarcinoma and squamous carcinoma, i.e., two types of lung cancers, using a large dataset containing thousands of lung microscopic tissue images. It achieves 90.3% accuracy by hashing and retrieving multiple cues of half-million cells.