In this work, we present the concept of Continuous Search (CS), which objective is to allow any user to eventually get top performance from their constraint solver. Unlike previous approaches, Continuous Search does not require the disposal of a large set of representative instances to properly train and learn parameters. It only assumes that once the solver runs in a real situation (often called production mode), instances will come over time, and allow for proper offline continuous training. The objective is therefore not to instantly provide good parameters for top performance, but to take advantage of the real situation to train in the background and improve the performances of the system in an incremental manner.