Virtual screening (VS) is a critical step in computer-aided drug discovery, aiming to identify molecules that bind to a specific target protein. Traditional VS methods, such as docking, are often too time-consuming to efficiently screen large-scale molecular databases. Recent advances in deep learning have demonstrated that learning vector representations for both proteins and molecules using contrastive learning can outperform traditional docking methods. However, considering that the target databases often contain billions of molecules, real-valued vector representations adopted by existing methods can still incur large memory and time cost in VS. To address this problem, we propose DrugHash, a hashing-based contrastive learning method for VS. DrugHash formulates VS as a retrieval task that leverages binary hash codes for efficient retrieval. In particular, DrugHash designs a simple yet effective hashing strategy to enable end-to-end learning of binary hash codes for both proteins and molecules, which can dramatically reduce the memory and time cost with higher accuracy compared with existing methods. Experimental results show that DrugHash can outperform existing methods to achieve state-of-the-art accuracy, with at least a 32 times reduction in memory cost and a 4.6 times improvement in speed.