As modern 5G systems are being deployed, researchers question whether they are sufficient for the oncoming decades of technological evolution. Growing numbers of interconnected intelligent devices put these networks under tremendous pressure, demanding their development. Paving the way for beyond 5G and 6G systems, commonly denoted by B5G herein, therefore means seeking enablers to increase efficiency from different perspectives. One novel look on this is the application of inexact computations where nine 9s reliability is not needed, for example, in non-critical mobile broadband traffic. The paradigm of Approximate Computing (AxC) focuses on such areas where constrained quality degradation results in savings that benefit the users and operators. This paper surveys the state-of-the-art publications on the intersection of AxC and B5G systems, identifying and emphasizing trends and tendencies in existing work and directions for future research. The work highlights resource allocation algorithms as particularly mesmerizing in the former, while research related to Intelligent Reflective Surfaces appears the most prominent in the latter. In both, problems are often NP-hard and, thus, only solvable using heuristics or approximations, Successive Convex Approximation and Reinforcement Learning are most frequently applied.