We address the problem of visual instance search, which consists to retrieve all the images within an dataset that contain a particular visual example provided to the system. The traditional approach of processing the image content for this task relied on extracting local low-level information within images that was “manually engineered” to be invariant to di↵erent image conditions. One of the most popular approaches uses the Bag of Visual Words (BoW) model on the local features to aggregate the local information into a single representation. Usually, a final reranking stage is included in the pipeline to refine the search results. Since the emergence of deep learning as the dominant technique in computer vision in 2012, much research attention has been focused on deriving image representations from Convolutional Neural Networks (CNN) models for the task of instance search as a “data driven” approach to designing image representations. However, one of the main challenges in the instance search task is the lack of annotated datasets to fit CNN models parameters. This work explores the capabilities of descriptors derived from pre-trained CNN models for image classification to address the task of instance retrieval. First, we conduct an investigation of the traditional bag of visual words encoding on local CNN features to produce a scalable image retrieval framework that generalizes well across di↵erent retrieval domains. Second, we propose to improve the capacity of the obtained representations by exploring an unsupervised fine-tuning strategy that allow us to obtain better performing representations at the price of losing the generalization of the representations. Finally, we propose using visual attention models to weight the contribution of the relevant parts of an image to obtain a very powerful image representation for instance retrieval without requiring the construction of a large and suitable training dataset for fine-tuning CNN architectures.