The generation of content-based text maps is an important issue to support exploration of information and to help find relevant reading material in increasingly complex document databases. Most techniques that help relate or visualize texts rely on a vector representation that is, at its best, ad-hoc as to its parameterization. This paper presents a novel approach capable of generating a map of documents without the painstaking pre-processing steps, by comparing text against text through an approximation of the Kolmogorov complexity. The similarity measure taken from that analysis is then used to map data in 2D by applying fast multidimensional projection techniques (instead of dimensionality reduction or random initial point placement). The resulting maps show a high degree of content separation and good grouping of similar documents. The approach can be used to map text collections in a variety of applications and the map can be interacted with to further explore text groups. By avoiding vector representation our technique decreases the bias characteristic of that approach and the need for user knowledge of the process. The approach also lends itself to incremental processing for reduction of computational costs.