Coreference Resolution has been posing an ongoing challenge to researchers for more than 50 years. It is the task of grouping mentions (concrete or abstract references, represented as textual spans) into clusters representing entities. The approaches for solving this problem have been manifold and range from rule-based approaches (Lee et al., 2013) over classical machine learning approaches (Rahman and Ng, 2009) to modern approaches based on Deep Learning (Lee et al., 2017; Joshi et al., 2020). Coreference Resolution can act as a ”glue” between information that is extracted on a local level (usually sentences) in order to obtain representations for an entire document or a collection of documents. This enables many interesting downstream applications such as the creation of character networks (Elson et al., 2010; Krug, 2020) or tracking of events involving central objects in textual media (such as the dagger in Emilia Galotti) (Hatzel and Biemann, 2021). The transfer of existing approaches to new domains or types of text usually comes with a drop in performance. In this work, we examine the performance of a rule-based and an end-to-end Deep Learning algorithm and their adaptability to the domain of German fairy tales. These experiments should provide insight into: a) the drop experienced from one kind of texts to another b) the reliability of state-of-the-art Deep Learning approaches compared to rule-based approaches for a change of texts and c) Capabilities for the adaptation to mitigate this natural drop in performance. This helps to estimate the required amount of manual work that is to be expected when transferring to a new kind of text, especially when the new type features a low number of annotated documents.