Anomaly detection is an important task in industrial applications. However, designing an accurate anomaly detector can be very challenging in settings where anomalous labels are sparse or, in the worst case, missing in the training data. To mitigate this issue of a lack of anomalous labels in the domain of interest, existing approaches use transfer learning, leveraging information from anomalous samples in a closely related domain. Although previous studies have shown good results from applying transfer learning, they do not specifically address the issue of high false-positive rates, especially in industrial settings. High false-positive rates can arise from misleading information present in uninformative features. Inspired by this observation, the paper focuses on identifying key input features—termed as such due to their strong predictability in anomaly detection. A transfer learning approach is introduced that leverages the optimal \(f_{\beta}\) score for key feature estimation. This approach involves a weight vector to amplify key features and attenuate uninformative inputs during prediction. We demonstrate the capabilities of our proposed method through an industrial application: anomaly detection for rotating machinery. Based on our findings, anomaly detection algorithms that utilize data-driven features obtained through the proposed method outperform detectors based on features identified by domain experts. More importantly, our proposed framework can work with any downstream unsupervised anomaly detection algorithm, allowing us to freely choose the best algorithm for the anomaly detection task.