Entity search plays a crucial role in various information access domains, where users seek information about specific entities. Despite significant research efforts to improve entity search methods, the availability of large-scale datasets and resources has been limited. In this work, we present the LaQuE (Large-scale Query collection for Entity search) dataset, a curated resource for entity search that includes real-user queries based on the ORCAS collection. LaQuE is a large-scale dataset and it is suitable for training neural models for entity search. We release the LaQuE dataset and report the performance of various retrievers, including traditional bag-of-words-based methods and complex pre-trained neural models, to establish strong baselines for this dataset. We show that training on the LaQuE dataset proves more effective for entity retrieval than using datasets intended for other tasks. We also categorize the released queries based on their popularity and difficulty, encouraging research on more challenging queries for the entity search task.