---
title: "Webinar: Too Hot, Too Cold, or Past Midnight? Statistical Considerations in Lead Optimization from Goldilocks & Cinderella"
description: Virtual screening to discover potential new ligands for the protein of interest in a drug discovery project and predicting the binding affinity of those new ligands are two key tasks in drug discovery. These tasks are usually performed by separate methods, relying on different methods of molecular representation. This talk presents an integrated approach to both problems using OpenEye’s core science in molecular shape and color; predicting ligand binding affinity using similarity in shape and color, then using the model object from affinity prediction to perform ultra-large scale virtual screening, also using similarity in shape and color. The talk also presents a new approach to molecular design using an understanding of the relationship between color features and binding affinity.
---

[On-Demand Webinars ](https://www.eyesopen.com/webinars/recordings)

# [Webinar: Too Hot, Too Cold, or Past Midnight? Statistical Considerations in Lead Optimization from Goldilocks & Cinderella](https://www.eyesopen.com/webinars/recordings/webinar-too-hot-too-cold-or-past-midnight-statistical-considerations-in-lead-optimization-from-goldilocks-cinderella)

 Written by [OpenEye Scientific](https://www.eyesopen.com/webinars/recordings/author/openeye-scientific) | Jun 1, 2026 7:32:07 PM

# Webinar: Too Hot, Too Cold, or Past Midnight?  Statistical Considerations in Lead Optimization from Goldilocks & Cinderella

**About this session**

In lead optimization, every synthesis decision counts, so reliable binding affinity predictions can mean the difference between a wasted cycle and a real breakthrough. However, traditional free energy methods are often too slow or compute-intensive to keep pace with today's project timelines.

Join us to discover FE-NES, a nonequilibrium alchemical method that delivers high-throughput relative binding free energy (RBFE) calculations with far less sampling than equilibrium approaches like FEP or TI. We will also present a reduced-sampling variant, Rapid FE-NES, that produces affinity predictions 3x faster than default FE-NES, enabling scaling into thousands of molecules with ease. We'll share insights on how Rapid FE-NES can effectively balance accuracy and scale, and show how adaptive compute allocation can help you prioritize the right molecules faster than ever.

[View full post](https://www.eyesopen.com/webinars/recordings/webinar-too-hot-too-cold-or-past-midnight-statistical-considerations-in-lead-optimization-from-goldilocks-cinderella)

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