The exponential growth of visual data demands robust Content-Based Image Retrieval (CBIR) systems that ac- curately and efficiently retrieve relevant images. In this paper, we present a novel CBIR framework that integrates AutoEncoders for latent feature extraction with hashing and Vantage-Point Trees (VP-Trees) for efficient similarity search. Experimental results on a publicly available dataset demonstrate significant improvements in retrieval precision and computational efficiency.