Exponential smoothing methods are flexible procedures for univariate time series forecasting, developed in the 1960 s. Most recent developments based on these models use bagging to improve forecast quality. One of these implementations, BaggedETS, developed in 2016, brought improvements in forecast quality and is distributed through the forecast package for R. A posterior implementation, BaggedClusterETS, adds clustering and validation steps to address the covariance effect associated with bagging. The proposal resulted in further accuracy improvements. This work delves into three extensions of the aforementioned methods: the first studies the effects of the maximum entropy bootstrap on the BaggedETS. The second explores different dissimilarity measures to construct the clusters in BaggedClusterETS. The third studies a simplified version of BaggedClusterETS, where the validation and selection steps are removed, and using only the medoids for bagging. To test these proposals, 21 time series from civil aviation and energy consumption were used.