Implicit measures have contributed to important insights in almost every area of psychology. However, various issues and challenges remain concerning their use, one of which is their considerable variation in reliability, with many implicit measures having questionable reliability. The goal of the present investigation was to examine an overlooked consequence of this liability with respect to replication, when such implicit measures are used as dependent variables in experimental studies. Using a Monte Carlo simulation, the authors demonstrate that a higher level of unreliability in such dependent variables is associated with substantially lower levels of replicability. The results imply that this overlooked consequence can have far-reaching repercussions for the development of a cumulative science. The authors recommend the routine assessment and reporting of the reliability of implicit measures and also urge the improvement of implicit measures with low reliability.