Large language models achieve high few-shot performance on many but not all downstream tasks. The interaction between pretraining and downstream data is commonly assumed to influence this variance: a task with data that is more similar to a model's pretraining dataset is assumed to be easier for that model. We test whether general textual similarity measures (embedding-, token- and model-based) correlate with large language model performance through a large-scale comparison of the Pile and C4 pretraining datasets with downstream benchmarks. Surprisingly, we find no correlation between performance and similarity across various models and dataset similarities.