Block-sorting is an innovative ompression mechanism introduced in 1994 by Burrows and Wheeler. It involves three steps: permuting the input one block at a time through the use of the Burrows-Wheeler Transform (BWT ); applying a Move-To-Front (MTF ) transform to each of the permuted blocks; and then entropy coding the output with a Huffman or arithmetic coder. Until now, block-sorting implementations have assumed that the input message is a sequence of characters. In this paper we extend the block-sorting mechanism to word-based models. We also consider other transformations as an alternative to MTF, and are able to show improved compression results compared to MTF. For large files of text, the combination of word-based modelling, BWT, and MTF-like transformations allows excellent compression effectiveness to be attained within reasonable resource costs.