Most autonomous driving and robotic applications require retrieving map data around the vehicle's current location. Those maps can cover large areas and are often stored in a compressed form to save memory and allow for efficient transmission. In this paper, we address the problem of place recognition in a compressed point cloud map. To this end, we propose a novel deep neural network architecture that directly operates on a compressed feature representation produced by a compression encoder. This enables us to bypass compute-heavy decompression of the map and exploits the compact as well as descriptive nature of the compressed features. Additionally, we propose an alternative to the commonly used NetVLAD layer to aggregate local descriptors. Here, we utilize an attention mechanism between local features and a latent code. Our experiments suggest that this produces a more descriptive feature representation of the point clouds for place recognition. We experimentally validate all architectural choices we made by our ablation studies and compare our performance to other state-of-the-art baselines on two commonly used datasets.