Isolation Forests are one of the most successful outlier detection techniques: they isolate outliers by performing random splits in each node. It has been recently shown that a trained Random Forest-based model can also be used to define and extract informative distance measures between objects. Although their success has been shown mainly in the clustering field, we propose to extract these pairwise distances between the objects from an Isolation Forest and use them as input to a distance or density-based outlier detector. We show that the extracted distances from Isolation Forests are able to describe outliers meaningfully. We evaluate our technique on ten benchmark datasets for outlier detection: we employ three different distance measures and evaluate the obtained representation using a density-based classifier, the Local Outlier Factor. We also compare the methodology to the standard Isolation Forests scheme.