In the data-driven era, dataset search has become a critical task in data science and engineering. Traditional spatial dataset search methods primarily rely on keyword or range queries, which are insufficient for capturing user intent expressed through exemplar datasets. To address this gap, this paper investigates the problem of top-k spatial dataset search using exemplar datasets as input. A novel grayscale image-based similarity model is first proposed, which maps the spatial distribution of datasets into grayscale images to capture detailed distribution features. Based on this model, a baseline search scheme (GIDS) is proposed. To further improve the search efficiency, an optimized search scheme (GIDS +) is introduced, which incorporates two key optimization strategies: a Morton code-based strategy to accelerate similarity calculations and a \(\omega \) -MSDtree-based strategy to enable efficient pruning during candidate filtering. Experiments conducted in two real-world spatial data repositories demonstrate that the proposed methods outperform existing approaches in search efficiency, providing a new solution for spatial dataset search.