With the growth of online fashion platforms and independent content creators, there is a growing interest in visually searching for similar clothing items as shown online. In real-world settings, clothes are often covered by other objects, making retrieval challenging. To make fashion image retrieval more robust, we explore fashion image retrieval with occlusion. We conducted various experiments on the In-shop Clothes Retrieval dataset, a subset of the DeepFashion benchmark. We constructed variations of the dataset with different occlusion types, including various sizes and locations of MSCOCO objects and object masks to simulate realistic occlusion circumstances. We evaluate the zero-shot and fine-tuned performance of the state-of-the-art models on these datasets and observe performance drop. We observe that fine-tuning models on one occluded dataset makes the model more robust to other occlusion types and reduces performance drop. The dataset used in this paper can be found in https://bit.ly/4749Mbo .