A novel hashing scheme based on a deep network architecture is proposed to tackle semantic similarity problems. The proposed methodology utilises the ability of deep networks to learn nonlinear representations of the input features. The equivalence of the neuron layer and the sigmoid smoothed hash functions is introduced, and by incorporating the saturation and orthogonality regulariser, the final compact binary embeddings can be achieved. The experiments illustrate that the proposed scheme exhibits superior improvement compared with conventional hashing methods.