Given a sketch query from a previously unseen category, the goal of zero-shot sketch-based image retrieval~(ZS-SBIR) is to retrieve semantically meaningful images from a given database. The knowledge-gap between the seen and unseen categories along with sketch-image domain shift makes this an extremely challenging problem. In this work, we propose a novel framework which decomposes each image and sketch into its domain-independent content and a domain, as well as data-dependent variation/style component. Specifically, given a query sketch and a search set of images, we utilize the image specific styles to guide the generation of fake images using the query content to be used for retrieval. Extensive experiments on two large-scale sketch-image datasets, Sketchy extended and TU-Berlin show that the proposed approach performs better or comparable to the state-of-the-art in both ZS-SBIR and generalized ZS-SBIR protocols.