Control scenarios have been identified where the use of randomized design may substantially improve the performance of dynamical decoupling methods (Santos and Viola 2006 Phys. Rev. Lett. 97 150501). Here, by focusing on the suppression of internal unwanted interactions in closed quantum systems, we review and further elaborate on the advantages of randomization at long evolution times. By way of illustration, special emphasis is devoted to isolated Heisenberg chains of coupled spin-1/2 particles. In particular, for nearest-neighbor interactions, two types of decoupling cycles are contrasted: inefficient averaging, whereby the number of control actions increases exponentially with the system size, and efficient averaging associated to a fixed-size control group. The latter allows for analytical and numerical studies of efficient decoupling schemes created by exploiting and merging together randomization and deterministic strategies, such as symmetrization, concatenation and cyclic permutations. Notably, sequences capable of removing interactions up to third order in the achievable control timescale are explicitly constructed, and a numerical algorithm to search for optimal decoupling sequences is proposed. The consequences of faulty controls in deterministic versus randomized schemes are also analyzed.