Vector-Quantized Variational Autoencoders (VQ-VAE)[1] provide an unsupervised model for learning discrete representations by combining vector quantization and autoencoders. In this paper, we study the use of VQ-VAE for representation learning of downstream tasks, such as image retrieval. First, we describe the VQ-VAE in the context of an information-theoretic framework. Then, we show that the regularization effect on the learned representation is determined by the size of the embedded codebook before the training. As a result, we introduce a hyperparameter to balance the strength of the vector quantizer and the reconstruction error. By tuning the hyperparameter, the embedded bottleneck quantizer is used as a regularizer that forces the output of the encoder to share a constrained coding space. With that, the learned latent features better preserve the similarity relations of the data space. Finally, we incorporate the product quantizer into the bottleneck stage of VQ-VAE and use it as an end-to-end unsupervised learning model for image retrieval tasks. The product quantizer has the advantage of generating large and structured codebooks. Fast retrieval can be achieved by using lookup tables that store the distance between any pair of sub-codewords. State-of-the-art retrieval results are achieved by the proposed codebooks.