With GPS data becoming commonplace through smartphones and other such devices, the need to support systems that are location-aware continues to increase. Just over half of all search queries from mobile devices have local intent, making location-aware search an increasingly important problem. Traditional Information Retrieval systems are able to return documents based on keywords, but this is insufficient for geographically motivated search tasks. This thesis investigates the efficiency and effectiveness trade-offs between two general types of geographical search queries, range queries and $k$-nearest-neighbour queries, for common web search tasks. We test and analyse state-of-the-art spatial-textual indexing and search algorithms for both query types across two large datasets. Finally, a rank-safe dynamic pruning algorithm is presented, which is shown to outperform the current tightly-coupled indexes used for top-$k$ location-aware queries