Airbnb has transformed the way people travel around the globe, but providing guests with relevant options in their search results has become increasingly complex due to the diverse locations and property types in their inventory. To address this challenge, Airbnb shifted from using simple heuristics to advanced machine learning and reinforcement learning techniques to transform their location retrieval process. Initially, Airbnb relied on heuristics to define map areas based on the type of search, but these heuristics had limitations and couldn't differentiate between different types of searches or adapt well to new data. Airbnb then explored statistics to improve location retrieval by building a dataset for each travel destination that recorded where guests booked listings when searching for that destination. However, this statistical approach still had limitations and treated all searches for a location the same, regardless of specific search parameters. This led Airbnb to believe that location retrieval may require more advanced techniques such as machine learning. Airbnb constructed a machine learning model that could learn from various search parameters, such as the number of guests and stay duration, and predict more relevant map areas for each search. The machine learning system increased the recall of booked listings by 7.12% and reduced the size of the retrieval map area by 40.83%, resulting in a cumulative impact of +1.8% in uncancelled bookers on the platform. Airbnb then introduced reinforcement learning to the location retrieval process, allowing the system to continuously learn from guest interactions and adjust the retrieval map area based on guest booking behavior. The reinforcement learning system successfully explored more for less-traveled locations and explored less for locations that are often searched and booked, resulting in a cumulative 0.51% increase in uncanceled bookers and 0.71% increase in 5-star trip rate. Airbnb's journey from simple heuristics to sophisticated machine learning and reinforcement learning models demonstrates the power of data-driven approaches in transforming complex systems. The transformation cumulatively results in a 2.66% increase in uncanceled bookers, a major achievement for a company operating at Airbnb's scale.