This paper addresses the problem of video-level object instance search, which aims to retrieve the videos in the database that contain a given query object instance. Without prior knowledge about "when" and "where" an object of interest may appear in a video, determining "whether" a video contains the target object is computationally prohibitive, as it requires exhaustively matching the query against all possible spatial-temporal locations in each video that an object may appear. To alleviate the computational and memory cost, we propose the Reconstruction-based Object SEarch (ROSE) method.It characterizes a huge corpus of features of possible spatial-temporal locations in the video into the parameters of the reconstruction model. Since the memory cost of storing reconstruction model is much less than that of storing features of possible spatial-temporal locations in the video, the efficiency of the search is significantly boosted. Comprehensive experiments on three benchmark datasets demonstrate the promising performance of the proposed ROSE method.