Place recognition in colonoscopy is needed for various reasons. 1) If a certain region needs to be rechecked during an endoscopy, the endoscopist needs to re-localize the camera accurately to the region of interest. 2) Place recognition is needed for same-patient follow-up colonoscopy to localize the region where a polyp was cut off. 3) Recent development in colonoscopic 3D reconstruction needs place recognition to establish long-range correspondence, e.g., for loop closure. However, traditional image retrieval techniques do not generalize well in colonic images. Moreover, although place recognition or instance-level image retrieval is a widely researched topic in computer vision and several benchmarks have been published for it, there has been no specific research or benchmarks in endoscopic images, which are significantly different from common images used in traditional computer vision tasks. In this paper we present a testing dataset with manually labeled groundtruth which comprises 10126 images from 20 colonoscopic subsequences. We perform an extensive evaluation on different existing place recognition techniques using different metrics.