In most video coding standards, motion estimation becomes the most time-consuming subsystem. Consequently, in the last few years, a great deal of effort has been devoted to the research of novel algorithms capable of saving computations with minimal effects on the coding quality. Adaptive algorithms and particularly multipattern solutions, have evolved as the most robust general-purpose solutions owing to two main reasons: 1) real video sequences usually exhibit a wide-range of motion content, from uniform to random, and 2) a vast amount of coding applications have appeared demanding different degrees of coding quality. In this study, we propose an adaptive algorithm, called motion classification-based search (MCS), which makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits them. The MCS has been experimentally assessed for a comprehensive set of selected video sequences and qualities. Our experimental results show that MCS notably reduces the computational cost up to 55% and 84% in search points, with respect to two state-of-the-art methods, while maintaining the quality.