The rapid growth in digital content has created an urgent need for efficient object lookup and retrieval systems. This paper presents a novel unsupervised contrastive learning approach that significantly improves the speed and accuracy of object retrieval operations. Our methodology eliminates the need for labeled training data by leveraging contrastive learning principles to learn discriminative feature representations directly from unlabeled image pairs. Through extensive experimentation, we demonstrate that our unsupervised approach achieves comparable or superior performance to supervised alternatives while substantially reducing computational overhead and data preparation requirements. The proposed method excels particularly in Similarity Grid Accuracy metrics, showing remarkable precision in identifying regions of high similarity between query objects and target images. Our approach achieves this efficiency through an innovative feature extraction mechanism that optimizes the trade-off between computational cost and retrieval accuracy. The experimental results validate the effectiveness of our method across diverse object categories and retrieval scenarios, establishing its practical utility for real-world applications. This work contributes to the field by demonstrating that unsupervised contrastive learning can be effectively leveraged to create efficient and scalable object lookup systems, potentially transforming how we approach large-scale visual search and retrieval tasks.